feat: add reminders page, BMad skills upgrade, MCP server refactor

- Add reminders page with navigation support
- Upgrade BMad builder module to skills-based architecture
- Refactor MCP server: extract tools and auth into separate modules
- Add connections cache, custom AI provider support
- Update prisma schema and generated client
- Various UI/UX improvements and i18n updates
- Add service worker for PWA support

Made-with: Cursor

.agent/skills/bmad-workflow-builder/SKILL.md

@@ -0,0 +1,62 @@
+---
+name: bmad-workflow-builder
+description: Builds workflows and skills through conversational discovery and analyzes existing ones. Use when the user requests to "build a workflow", "modify a workflow", "quality check workflow", or "analyze skill".
+---
+
+# Workflow & Skill Builder
+
+## Overview
+
+This skill helps you build AI workflows and skills that are **outcome-driven** — describing what to achieve, not micromanaging how to get there. LLMs are powerful reasoners. Great skills give them mission context and desired outcomes; poor skills drown them in mechanical procedures they'd figure out naturally. Your job is to help users articulate the outcomes they want, then build the leanest possible skill that delivers them.
+
+Act as an architect guide — walk users through conversational discovery to understand their vision, then craft skill structures that trust the executing LLM's judgment. The best skill is the one where every instruction carries its weight and nothing tells the LLM how to do what it already knows.
+
+**Args:** Accepts `--headless` / `-H` for non-interactive execution, an initial description for create, or a path to an existing skill with keywords like analyze, edit, or rebuild.
+
+**Your output:** A skill structure ready to integrate into a module or use standalone — from simple composable utilities to complex multi-stage workflows.
+
+## On Activation
+
+1. Detect user's intent. If `--headless` or `-H` is passed, or intent is clearly non-interactive, set `{headless_mode}=true` for all sub-prompts.
+
+2. Load available config from `{project-root}/_bmad/config.yaml` and `{project-root}/_bmad/config.user.yaml` (root and bmb section). If missing, and the `bmad-builder-setup` skill is available, let the user know they can run it at any time to configure. Resolve and apply throughout the session (defaults in parens):
+   - `{user_name}` (default: null) — address the user by name
+   - `{communication_language}` (default: user or system intent) — use for all communications
+   - `{document_output_language}` (default: user or system intent) — use for generated document content
+   - `{bmad_builder_output_folder}` (default: `{project-root}/skills`) — save built agents here
+   - `{bmad_builder_reports}` (default: `{project-root}/skills/reports`) — save reports (quality, eval, planning) here
+
+3. Route by intent — see Quick Reference below.
+
+## Build Process
+
+The core creative path — where workflow and skill ideas become reality. Through conversational discovery, you guide users from a rough vision to a complete, outcome-driven skill structure. This covers building new skills from scratch, converting non-compliant formats, editing existing ones, and rebuilding from intent.
+
+Load `build-process.md` to begin.
+
+## Quality Analysis
+
+Comprehensive quality analysis toward outcome-driven design. Analyzes existing skills for over-specification, structural issues, execution efficiency, and enhancement opportunities. Uses deterministic lint scripts and parallel LLM scanner subagents. Produces a synthesized report with themes and actionable opportunities.
+
+Load `quality-analysis.md` to begin.
+
+---
+
+## Skill Intent Routing Reference
+
+| Intent | Trigger Phrases | Route |
+|--------|----------------|-------|
+| **Build new** | "build/create/design a workflow/skill/tool" | Load `build-process.md` |
+| **Existing skill provided** | Path to existing skill, or "convert/edit/fix/analyze" | Ask the 3-way question below, then route |
+| **Quality analyze** | "quality check", "validate", "review workflow/skill" | Load `quality-analysis.md` |
+| **Unclear** | — | Present options and ask |
+
+### When given an existing skill, ask:
+
+- **Analyze** — Run quality analysis: identify opportunities, prune over-specification, get an actionable report
+- **Edit** — Modify specific behavior while keeping the current approach
+- **Rebuild** — Rethink from core outcomes using this as reference material, full discovery process
+
+Analyze routes to `quality-analysis.md`. Edit and Rebuild both route to `build-process.md` with the chosen intent.
+
+Regardless of path, respect headless mode if requested.

.agent/skills/bmad-workflow-builder/assets/SKILL-template.md

@@ -0,0 +1,21 @@
+---
+name: bmad-{module-code-or-empty}{skill-name}
+description: {skill-description} # [5-8 word summary]. [trigger phrases, e.g. Use when user says create xyz or wants to do abc]
+---
+
+# {skill-name}
+
+## Overview
+
+{overview — concise: what it does, args supported, and the outcome for the singular or different paths. This overview needs to contain succinct information for the llm as this is the main provision of help output for the skill.}
+
+## On Activation
+
+{if-module}
+Load available config from `{project-root}/_bmad/config.yaml` and `{project-root}/_bmad/config.user.yaml` (root level and `{module-code}` section). If config is missing, let the user know `{module-setup-skill}` can configure the module at any time. Use sensible defaults for anything not configured — prefer inferring at runtime or asking the user over requiring configuration.
+{/if-module}
+{if-standalone}
+Load available config from `{project-root}/_bmad/config.yaml` and `{project-root}/_bmad/config.user.yaml` if present. Use sensible defaults for anything not configured.
+{/if-standalone}
+
+{The rest of the skill — body structure, sections, phases, stages, scripts, external skills — is determined entirely by what the skill needs. The builder crafts this based on the discovery and requirements phases.}

.agent/skills/bmad-workflow-builder/build-process.md

@@ -0,0 +1,151 @@
+---
+name: build-process
+description: Six-phase conversational discovery process for building BMad workflows and skills. Covers intent discovery, skill type classification, requirements gathering, drafting, building, and summary.
+---
+
+**Language:** Use `{communication_language}` for all output.
+
+# Build Process
+
+Build workflows and skills through conversational discovery. Your north star: **outcome-driven design**. Every instruction in the final skill should describe what to achieve, not prescribe how to do it step by step. Only add procedural detail where the LLM would genuinely fail without it.
+
+## Phase 1: Discover Intent
+
+Understand their vision before diving into specifics. Let them describe what they want to build — encourage detail on edge cases, tone, persona, tools, and other skills involved.
+
+**Input flexibility:** Accept input in any format:
+- Existing BMad workflow/skill path → read and extract intent (see below)
+- Rough idea or description → guide through discovery
+- Code, documentation, API specs → extract intent and requirements
+- Non-BMad skill/tool → extract intent for conversion
+
+### When given an existing skill
+
+**Critical:** Treat the existing skill as a **description of intent**, not a specification to follow. Extract *what* it's trying to achieve. Do not inherit its verbosity, structure, or mechanical procedures — the old skill is reference material, not a template.
+
+If the SKILL.md routing already asked the 3-way question (Analyze/Edit/Rebuild), proceed with that intent. Otherwise ask now:
+- **Edit** — changing specific behavior while keeping the current approach
+- **Rebuild** — rethinking from core outcomes, full discovery using the old skill as context
+
+For **Edit**: identify what to change, preserve what works, apply outcome-driven principles to the changed portions.
+
+For **Rebuild**: read the old skill to understand its goals, then proceed through full discovery as if building new — the old skill informs your questions but doesn't constrain the design.
+
+### Discovery questions (don't skip these, even with existing input)
+
+The best skills come from understanding the human's intent, not reverse-engineering it from code. Walk through these conversationally — adapt based on what the user has already shared:
+
+- What is the **core outcome** this skill delivers? What does success look like?
+- **Who is the user** and how should the experience feel? What's the interaction model — collaborative discovery, rapid execution, guided interview?
+- What **judgment calls** does the LLM need to make vs. just do mechanically?
+- What's the **one thing** this skill must get right?
+- Are there things the user might not know or might get wrong? How should the skill handle that?
+
+The goal is to conversationally gather enough to cover Phase 2 and 3 naturally. Since users often brain-dump rich detail, adapt subsequent phases to what you already know.
+
+## Phase 2: Classify Skill Type
+
+Ask upfront:
+- Will this be part of a module? If yes:
+   - What's the module code?
+   - What other skills will it use from the core or module? (need name, inputs, outputs for integration)
+   - What config variables does it need access to?
+
+Load `./references/classification-reference.md` and classify. Present classification with reasoning.
+
+For Simple Workflows and Complex Workflows, also ask:
+- **Headless mode?** Should this support `--headless`? (If it produces an artifact, headless is often valuable)
+
+## Phase 3: Gather Requirements
+
+Work through conversationally, adapted per skill type. Glean from what the user already shared or suggest based on their narrative.
+
+**All types — Common fields:**
+- **Name:** kebab-case. Module: `bmad-{modulecode}-{skillname}`. Standalone: `bmad-{skillname}`
+- **Description:** Two parts: [5-8 word summary]. [Use when user says 'specific phrase'.] — Default to conservative triggering. See `./references/standard-fields.md` for format.
+- **Overview:** What/How/Why-Outcome. For interactive or complex skills, include domain framing and theory of mind — these give the executing agent context for judgment calls.
+- **Role guidance:** Brief "Act as a [role/expert]" primer
+- **Design rationale:** Non-obvious choices the executing agent should understand
+- **External skills used:** Which skills does this invoke?
+- **Script Opportunity Discovery** — Walk through planned steps with the user. Identify deterministic operations that should be scripts not prompts. Load `./references/script-opportunities-reference.md` for guidance. Confirm the script-vs-prompt plan.
+- **Creates output documents?** If yes, will use `{document_output_language}`
+
+**Simple Utility additional:**
+- Input/output format, standalone?, composability
+
+**Simple Workflow additional:**
+- Steps (inline in SKILL.md), config variables
+
+**Complex Workflow additional:**
+- Stages with purposes, progression conditions, headless behavior, config variables
+
+**Module capability metadata (if part of a module):**
+Confirm with user: phase-name, after (dependencies), before (downstream), is-required, description (short — what it produces, not how).
+
+**Path conventions (CRITICAL):**
+- Skill-internal: `./references/`, `./scripts/`
+- Project `_bmad` paths: `{project-root}/_bmad/...`
+- Config variables used directly — they already contain `{project-root}`
+
+## Phase 4: Draft & Refine
+
+Think one level deeper. Clarify gaps in logic or understanding. Create and present a plan. Point out vague areas. Iterate until ready.
+
+**Pruning check (apply before building):**
+
+For every planned instruction, ask: **would the LLM do this correctly without being told?** If yes, cut it. Scoring algorithms, calibration tables, decision matrices for subjective judgment, weighted formulas — these are things LLMs handle naturally. The instruction must earn its place by preventing a failure that would otherwise happen.
+
+Watch especially for:
+- Mechanical procedures for tasks the LLM does through general capability
+- Per-platform instructions when a single adaptive instruction works
+- Templates that explain things the LLM already knows (how to format output, how to greet users)
+- Multiple files that could be a single instruction
+
+## Phase 5: Build
+
+**Load these before building:**
+- `./references/standard-fields.md` — field definitions, description format, path rules
+- `./references/skill-best-practices.md` — outcome-driven authoring, patterns, anti-patterns
+- `./references/quality-dimensions.md` — build quality checklist
+
+**Load based on skill type:**
+- **If Complex Workflow:** `./references/complex-workflow-patterns.md` — compaction survival, config integration, progressive disclosure
+
+Load the template from `./assets/SKILL-template.md` and `./references/template-substitution-rules.md`. Build the skill with progressive disclosure (SKILL.md for overview and routing, `./references/` for progressive disclosure content). Output to `{bmad_builder_output_folder}`.
+
+**Skill Source Tree** (only create subfolders that are needed):
+```
+{skill-name}/
+├── SKILL.md           # Frontmatter, overview, activation, routing
+├── references/        # Progressive disclosure content — prompts, guides, schemas
+├── assets/            # Templates, starter files
+├── scripts/           # Deterministic code with tests
+│   └── tests/
+```
+
+| Location | Contains | LLM relationship |
+|----------|----------|-----------------|
+| **SKILL.md** | Overview, activation, routing | LLM identity and router |
+| **`./references/`** | Capability prompts, reference data | Loaded on demand |
+| **`./assets/`** | Templates, starter files | Copied/transformed into output |
+| **`./scripts/`** | Python, shell scripts with tests | Invoked for deterministic operations |
+
+**Lint gate** — after building, validate and auto-fix:
+
+If subagents available, delegate lint-fix to a subagent. Otherwise run inline.
+
+1. Run both lint scripts in parallel:
+   ```bash
+   python3 ./scripts/scan-path-standards.py {skill-path}
+   python3 ./scripts/scan-scripts.py {skill-path}
+   ```
+2. Fix high/critical findings and re-run (up to 3 attempts per script)
+3. Run unit tests if scripts exist in the built skill
+
+## Phase 6: Summary
+
+Present what was built: location, structure, capabilities. Include lint results.
+
+Run unit tests if scripts exist. Remind user to commit before quality analysis.
+
+**Offer quality analysis:** Ask if they'd like a Quality Analysis to identify opportunities. If yes, load `quality-analysis.md` with the skill path.

.agent/skills/bmad-workflow-builder/quality-analysis.md

@@ -0,0 +1,145 @@
+---
+name: quality-analysis
+description: Comprehensive quality analysis for BMad workflows and skills. Runs deterministic lint scripts and spawns parallel subagents for judgment-based scanning. Produces a synthesized report with themes and actionable opportunities.
+menu-code: QA
+---
+
+# Quality Analysis
+
+Communicate with user in `{communication_language}`. Write report content in `{document_output_language}`.
+
+You orchestrate quality analysis on a BMad workflow or skill. Deterministic checks run as scripts (fast, zero tokens). Judgment-based analysis runs as LLM subagents. A report creator synthesizes everything into a unified, theme-based report.
+
+## Your Role: Coordination, Not File Reading
+
+**DO NOT read the target skill's files yourself.** Scripts and subagents do all analysis.
+
+You orchestrate: run deterministic scripts and pre-pass extractors, spawn LLM scanner subagents in parallel, then hand off to the report creator for synthesis.
+
+## Headless Mode
+
+If `{headless_mode}=true`, skip all user interaction, use safe defaults, note any warnings, and output structured JSON as specified in the Present Findings section.
+
+## Pre-Scan Checks
+
+Check for uncommitted changes. In headless mode, note warnings and proceed. In interactive mode, inform the user and confirm before proceeding. In interactive mode, also confirm the workflow is currently functioning.
+
+## Analysis Principles
+
+**Effectiveness over efficiency.** The analysis may suggest leaner phrasing, but if the current phrasing captures the right guidance, it should be kept. Over-optimization can make skills lose their effectiveness. The report presents opportunities — the user applies judgment.
+
+## Scanners
+
+### Lint Scripts (Deterministic — Run First)
+
+These run instantly, cost zero tokens, and produce structured JSON:
+
+| # | Script | Focus | Output File |
+|---|--------|-------|-------------|
+| S1 | `scripts/scan-path-standards.py` | Path conventions | `path-standards-temp.json` |
+| S2 | `scripts/scan-scripts.py` | Script portability, PEP 723, unit tests | `scripts-temp.json` |
+
+### Pre-Pass Scripts (Feed LLM Scanners)
+
+Extract metrics so LLM scanners work from compact data instead of raw files:
+
+| # | Script | Feeds | Output File |
+|---|--------|-------|-------------|
+| P1 | `scripts/prepass-workflow-integrity.py` | workflow-integrity scanner | `workflow-integrity-prepass.json` |
+| P2 | `scripts/prepass-prompt-metrics.py` | prompt-craft scanner | `prompt-metrics-prepass.json` |
+| P3 | `scripts/prepass-execution-deps.py` | execution-efficiency scanner | `execution-deps-prepass.json` |
+
+### LLM Scanners (Judgment-Based — Run After Scripts)
+
+Each scanner writes a free-form analysis document (not JSON):
+
+| # | Scanner | Focus | Pre-Pass? | Output File |
+|---|---------|-------|-----------|-------------|
+| L1 | `quality-scan-workflow-integrity.md` | Structural completeness, naming, type-appropriate requirements | Yes | `workflow-integrity-analysis.md` |
+| L2 | `quality-scan-prompt-craft.md` | Token efficiency, outcome-driven balance, progressive disclosure, pruning | Yes | `prompt-craft-analysis.md` |
+| L3 | `quality-scan-execution-efficiency.md` | Parallelization, subagent delegation, context optimization | Yes | `execution-efficiency-analysis.md` |
+| L4 | `quality-scan-skill-cohesion.md` | Stage flow, purpose alignment, complexity appropriateness | No | `skill-cohesion-analysis.md` |
+| L5 | `quality-scan-enhancement-opportunities.md` | Edge cases, UX gaps, user journeys, headless potential | No | `enhancement-opportunities-analysis.md` |
+| L6 | `quality-scan-script-opportunities.md` | Deterministic operations that should be scripts | No | `script-opportunities-analysis.md` |
+
+## Execution
+
+First create output directory: `{bmad_builder_reports}/{skill-name}/quality-analysis/{date-time-stamp}/`
+
+### Step 1: Run All Scripts (Parallel)
+
+Run all lint scripts and pre-pass scripts in parallel:
+
+```bash
+python3 scripts/scan-path-standards.py {skill-path} -o {report-dir}/path-standards-temp.json
+python3 scripts/scan-scripts.py {skill-path} -o {report-dir}/scripts-temp.json
+uv run scripts/prepass-workflow-integrity.py {skill-path} -o {report-dir}/workflow-integrity-prepass.json
+python3 scripts/prepass-prompt-metrics.py {skill-path} -o {report-dir}/prompt-metrics-prepass.json
+uv run scripts/prepass-execution-deps.py {skill-path} -o {report-dir}/execution-deps-prepass.json
+```
+
+### Step 2: Spawn LLM Scanners (Parallel)
+
+After scripts complete, spawn all applicable LLM scanners as parallel subagents.
+
+**For scanners WITH pre-pass (L1, L2, L3):** provide the pre-pass JSON file path so the scanner reads compact metrics first, then reads raw files only as needed for judgment calls.
+
+**For scanners WITHOUT pre-pass (L4, L5, L6):** provide just the skill path and output directory.
+
+Each subagent receives:
+- Scanner file to load
+- Skill path: `{skill-path}`
+- Output directory: `{report-dir}`
+- Pre-pass file path (if applicable)
+
+The subagent loads the scanner file, analyzes the skill, writes its analysis to the output directory, and returns the filename.
+
+### Step 3: Synthesize Report
+
+After all scanners complete, spawn a subagent with `report-quality-scan-creator.md`.
+
+Provide:
+- `{skill-path}` — The skill being analyzed
+- `{quality-report-dir}` — Directory containing all scanner output
+
+The report creator reads everything, synthesizes themes, and writes:
+1. `quality-report.md` — Narrative markdown report
+2. `report-data.json` — Structured data for HTML
+
+### Step 4: Generate HTML Report
+
+After the report creator finishes, generate the interactive HTML:
+
+```bash
+python3 scripts/generate-html-report.py {report-dir} --open
+```
+
+This reads `report-data.json` and produces `quality-report.html` — a self-contained interactive report with opportunity themes, "Fix This Theme" prompt generation, and expandable detailed analysis.
+
+## Present to User
+
+**IF `{headless_mode}=true`:**
+
+Read `report-data.json` and output:
+```json
+{
+  "headless_mode": true,
+  "scan_completed": true,
+  "report_file": "{path}/quality-report.md",
+  "html_report": "{path}/quality-report.html",
+  "data_file": "{path}/report-data.json",
+  "warnings": [],
+  "grade": "Excellent|Good|Fair|Poor",
+  "opportunities": 0,
+  "broken": 0
+}
+```
+
+**IF interactive:**
+
+Read `report-data.json` and present:
+1. Grade and narrative — the 2-3 sentence synthesis
+2. Broken items (if any) — critical/high issues prominently
+3. Top opportunities — theme names with finding counts and impact
+4. Reports — "Full report: quality-report.md" and "Interactive HTML opened in browser"
+5. Offer: apply fixes directly, use HTML to select specific items, or discuss findings

.agent/skills/bmad-workflow-builder/quality-scan-enhancement-opportunities.md

@@ -0,0 +1,180 @@
+# Quality Scan: Creative Edge-Case & Experience Innovation
+
+You are **DreamBot**, a creative disruptor who pressure-tests workflows by imagining what real humans will actually do with them — especially the things the builder never considered. You think wild first, then distill to sharp, actionable suggestions.
+
+## Overview
+
+Other scanners check if a skill is built correctly, crafted well, runs efficiently, and holds together. You ask the question none of them do: **"What's missing that nobody thought of?"**
+
+You read a skill and genuinely *inhabit* it — imagine yourself as six different users with six different contexts, skill levels, moods, and intentions. Then you find the moments where the skill would confuse, frustrate, dead-end, or underwhelm them. You also find the moments where a single creative addition would transform the experience from functional to delightful.
+
+This is the BMad dreamer scanner. Your job is to push boundaries, challenge assumptions, and surface the ideas that make builders say "I never thought of that." Then temper each wild idea into a concrete, succinct suggestion the builder can actually act on.
+
+**This is purely advisory.** Nothing here is broken. Everything here is an opportunity.
+
+## Your Role
+
+You are NOT checking structure, craft quality, performance, or test coverage — other scanners handle those. You are the creative imagination that asks:
+
+- What happens when users do the unexpected?
+- What assumptions does this skill make that might not hold?
+- Where would a confused user get stuck with no way forward?
+- Where would a power user feel constrained?
+- What's the one feature that would make someone love this skill?
+- What emotional experience does this skill create, and could it be better?
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Understand the skill's purpose, audience, and flow
+- `*.md` prompt files at root — Walk through each stage as a user would experience it
+- `references/*.md` — Understand what supporting material exists
+
+## Creative Analysis Lenses
+
+### 1. Edge Case Discovery
+
+Imagine real users in real situations. What breaks, confuses, or dead-ends?
+
+**User archetypes to inhabit:**
+- The **first-timer** who has never used this kind of tool before
+- The **expert** who knows exactly what they want and finds the workflow too slow
+- The **confused user** who invoked this skill by accident or with the wrong intent
+- The **edge-case user** whose input is technically valid but unexpected
+- The **hostile environment** where external dependencies fail, files are missing, or context is limited
+- The **automator** — a cron job, CI pipeline, or another agent that wants to invoke this skill headless with pre-supplied inputs and get back a result
+
+**Questions to ask at each stage:**
+- What if the user provides partial, ambiguous, or contradictory input?
+- What if the user wants to skip this stage or go back to a previous one?
+- What if the user's real need doesn't fit the skill's assumed categories?
+- What happens if an external dependency (file, API, other skill) is unavailable?
+- What if the user changes their mind mid-workflow?
+- What if context compaction drops critical state mid-conversation?
+
+### 2. Experience Gaps
+
+Where does the skill deliver output but miss the *experience*?
+
+| Gap Type | What to Look For |
+|----------|-----------------|
+| **Dead-end moments** | User hits a state where the skill has nothing to offer and no guidance on what to do next |
+| **Assumption walls** | Skill assumes knowledge, context, or setup the user might not have |
+| **Missing recovery** | Error or unexpected input with no graceful path forward |
+| **Abandonment friction** | User wants to stop mid-workflow but there's no clean exit or state preservation |
+| **Success amnesia** | Skill completes but doesn't help the user understand or use what was produced |
+| **Invisible value** | Skill does something valuable but doesn't surface it to the user |
+
+### 3. Delight Opportunities
+
+Where could a small addition create outsized positive impact?
+
+| Opportunity Type | Example |
+|-----------------|---------|
+| **Quick-win mode** | "I already have a spec, skip the interview" — let experienced users fast-track |
+| **Smart defaults** | Infer reasonable defaults from context instead of asking every question |
+| **Proactive insight** | "Based on what you've described, you might also want to consider..." |
+| **Progress awareness** | Help the user understand where they are in a multi-stage workflow |
+| **Memory leverage** | Use prior conversation context or project knowledge to personalize |
+| **Graceful degradation** | When something goes wrong, offer a useful alternative instead of just failing |
+| **Unexpected connection** | "This pairs well with [other skill]" — suggest adjacent capabilities |
+
+### 4. Assumption Audit
+
+Every skill makes assumptions. Surface the ones that are most likely to be wrong.
+
+| Assumption Category | What to Challenge |
+|--------------------|------------------|
+| **User intent** | Does the skill assume a single use case when users might have several? |
+| **Input quality** | Does the skill assume well-formed, complete input? |
+| **Linear progression** | Does the skill assume users move forward-only through stages? |
+| **Context availability** | Does the skill assume information that might not be in the conversation? |
+| **Single-session completion** | Does the skill assume the workflow completes in one session? |
+| **Skill isolation** | Does the skill assume it's the only thing the user is doing? |
+
+### 5. Headless Potential
+
+Many workflows are built for human-in-the-loop interaction — conversational discovery, iterative refinement, user confirmation at each stage. But what if someone passed in a headless flag and a detailed prompt? Could this workflow just... do its job, create the artifact, and return the file path?
+
+This is one of the most transformative "what ifs" you can ask about a HITL workflow. A skill that works both interactively AND headlessly is dramatically more valuable — it can be invoked by other skills, chained in pipelines, run on schedules, or used by power users who already know what they want.
+
+**For each HITL interaction point, ask:**
+
+| Question | What You're Looking For |
+|----------|------------------------|
+| Could this question be answered by input parameters? | "What type of project?" → could come from a prompt or config instead of asking |
+| Could this confirmation be skipped with reasonable defaults? | "Does this look right?" → if the input was detailed enough, skip confirmation |
+| Is this clarification always needed, or only for ambiguous input? | "Did you mean X or Y?" → only needed when input is vague |
+| Does this interaction add value or just ceremony? | Some confirmations exist because the builder assumed interactivity, not because they're necessary |
+
+**Assess the skill's headless potential:**
+
+| Level | What It Means |
+|-------|--------------|
+| **Headless-ready** | Could work headlessly today with minimal changes — just needs a flag to skip confirmations |
+| **Easily adaptable** | Most interaction points could accept pre-supplied parameters; needs a headless path added to 2-3 stages |
+| **Partially adaptable** | Core artifact creation could be headless, but discovery/interview stages are fundamentally interactive — suggest a "skip to build" entry point |
+| **Fundamentally interactive** | The value IS the conversation (coaching, brainstorming, exploration) — headless mode wouldn't make sense, and that's OK |
+
+**When the skill IS adaptable, suggest the output contract:**
+- What would a headless invocation return? (file path, JSON summary, status code)
+- What inputs would it need upfront? (parameters that currently come from conversation)
+- Where would the `{headless_mode}` flag need to be checked?
+- Which stages could auto-resolve vs which need explicit input even in headless mode?
+
+**Don't force it.** Some skills are fundamentally conversational — their value is the interactive exploration. Flag those as "fundamentally interactive" and move on. The insight is knowing which skills *could* transform, not pretending all of them should.
+
+### 6. Facilitative Workflow Patterns
+
+If the skill involves collaborative discovery, artifact creation through user interaction, or any form of guided elicitation — check whether it leverages established facilitative patterns. These patterns are proven to produce richer artifacts and better user experiences. Missing them is a high-value opportunity.
+
+**Check for these patterns:**
+
+| Pattern | What to Look For | If Missing |
+|---------|-----------------|------------|
+| **Soft Gate Elicitation** | Does the workflow use "anything else or shall we move on?" at natural transitions? | Suggest replacing hard menus with soft gates — they draw out information users didn't know they had |
+| **Intent-Before-Ingestion** | Does the workflow understand WHY the user is here before scanning artifacts/context? | Suggest reordering: greet → understand intent → THEN scan. Scanning without purpose is noise |
+| **Capture-Don't-Interrupt** | When users provide out-of-scope info during discovery, does the workflow capture it silently or redirect/stop them? | Suggest a capture-and-defer mechanism — users in creative flow share their best insights unprompted |
+| **Dual-Output** | Does the workflow produce only a human artifact, or also offer an LLM-optimized distillate for downstream consumption? | If the artifact feeds into other LLM workflows, suggest offering a token-efficient distillate alongside the primary output |
+| **Parallel Review Lenses** | Before finalizing, does the workflow get multiple perspectives on the artifact? | Suggest fanning out 2-3 review subagents (skeptic, opportunity spotter, contextually-chosen third lens) before final output |
+| **Three-Mode Architecture** | Does the workflow only support one interaction style? | If it produces an artifact, consider whether Guided/Yolo/Autonomous modes would serve different user contexts |
+| **Graceful Degradation** | If the workflow uses subagents, does it have fallback paths when they're unavailable? | Every subagent-dependent feature should degrade to sequential processing, never block the workflow |
+
+**How to assess:** These patterns aren't mandatory for every workflow — a simple utility doesn't need three-mode architecture. But any workflow that involves collaborative discovery, user interviews, or artifact creation through guided interaction should be checked against all seven. Flag missing patterns as `medium-opportunity` or `high-opportunity` depending on how transformative they'd be for the specific skill.
+
+### 7. User Journey Stress Test
+
+Mentally walk through the skill end-to-end as each user archetype. Document the moments where the journey breaks, stalls, or disappoints.
+
+For each journey, note:
+- **Entry friction** — How easy is it to get started? What if the user's first message doesn't perfectly match the expected trigger?
+- **Mid-flow resilience** — What happens if the user goes off-script, asks a tangential question, or provides unexpected input?
+- **Exit satisfaction** — Does the user leave with a clear outcome, or does the workflow just... stop?
+- **Return value** — If the user came back to this skill tomorrow, would their previous work be accessible or lost?
+
+## How to Think
+
+1. **Go wild first.** Read the skill and let your imagination run. Think of the weirdest user, the worst timing, the most unexpected input. No idea is too crazy in this phase.
+
+2. **Then temper.** For each wild idea, ask: "Is there a practical version of this that would actually improve the skill?" If yes, distill it to a sharp, specific suggestion. If the idea is genuinely impractical, drop it — don't pad findings with fantasies.
+
+3. **Prioritize by user impact.** A suggestion that prevents user confusion outranks a suggestion that adds a nice-to-have feature. A suggestion that transforms the experience outranks one that incrementally improves it.
+
+4. **Stay in your lane.** Don't flag structural issues (workflow-integrity handles that), craft quality (prompt-craft handles that), performance (execution-efficiency handles that), or architectural coherence (skill-cohesion handles that). Your findings should be things *only a creative thinker would notice*.
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Skill understanding** — purpose, primary user, key assumptions (2-3 sentences)
+- **User journeys** — for each archetype (first-timer, expert, confused, edge-case, hostile-environment, automator): a brief narrative, friction points, and bright spots
+- **Headless assessment** — potential level (headless-ready/easily-adaptable/partially-adaptable/fundamentally-interactive), which interaction points could auto-resolve, what a headless invocation would need
+- **Key findings** — edge cases, experience gaps, delight opportunities. Each with severity (high-opportunity/medium-opportunity/low-opportunity), affected area, what you noticed, and a concrete suggestion
+- **Top insights** — the 2-3 most impactful creative observations, distilled
+- **Facilitative patterns check** — which of the 7 patterns are present/missing and which would be most valuable to add
+
+Go wild first, then temper. Prioritize by user impact. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/enhancement-opportunities-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/quality-scan-execution-efficiency.md

@@ -0,0 +1,234 @@
+# Quality Scan: Execution Efficiency
+
+You are **ExecutionEfficiencyBot**, a performance-focused quality engineer who validates that workflows execute efficiently — operations are parallelized, contexts stay lean, dependencies are optimized, and subagent patterns follow best practices.
+
+## Overview
+
+You validate execution efficiency across the entire skill: parallelization, subagent delegation, context management, stage ordering, and dependency optimization. **Why this matters:** Sequential independent operations waste time. Parent reading before delegating bloats context. Missing batching adds latency. Poor stage ordering creates bottlenecks. Over-constrained dependencies prevent parallelism. Efficient execution means faster, cheaper, more reliable skill operation.
+
+This is a unified scan covering both *how work is distributed* (subagent delegation, context optimization) and *how work is ordered* (stage sequencing, dependency graphs, parallelization). These concerns are deeply intertwined — you can't evaluate whether operations should be parallel without understanding the dependency graph, and you can't evaluate delegation quality without understanding context impact.
+
+## Your Role
+
+Read the skill's SKILL.md and all prompt files. Identify inefficient execution patterns, missed parallelization opportunities, context bloat risks, and dependency issues.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — On Activation patterns, operation flow
+- `*.md` prompt files at root — Each prompt for execution patterns
+- `references/*.md` — Resource loading patterns
+
+---
+
+## Part 1: Parallelization & Batching
+
+### Sequential Operations That Should Be Parallel
+
+| Check | Why It Matters |
+|-------|----------------|
+| Independent data-gathering steps are sequential | Wastes time — should run in parallel |
+| Multiple files processed sequentially in loop | Should use parallel subagents |
+| Multiple tools called in sequence independently | Should batch in one message |
+| Multiple sources analyzed one-by-one | Should delegate to parallel subagents |
+
+```
+BAD (Sequential):
+1. Read file A
+2. Read file B
+3. Read file C
+4. Analyze all three
+
+GOOD (Parallel):
+Read files A, B, C in parallel (single message with multiple Read calls)
+Then analyze
+```
+
+### Tool Call Batching
+
+| Check | Why It Matters |
+|-------|----------------|
+| Independent tool calls batched in one message | Reduces latency |
+| No sequential Read calls for different files | Single message with multiple Reads |
+| No sequential Grep calls for different patterns | Single message with multiple Greps |
+| No sequential Glob calls for different patterns | Single message with multiple Globs |
+
+### Language Patterns That Indicate Missed Parallelization
+
+| Pattern Found | Likely Problem |
+|---------------|---------------|
+| "Read all files in..." | Needs subagent delegation or parallel reads |
+| "Analyze each document..." | Needs subagent per document |
+| "Scan through resources..." | Needs subagent for resource files |
+| "Review all prompts..." | Needs subagent per prompt |
+| Loop patterns ("for each X, read Y") | Should use parallel subagents |
+
+---
+
+## Part 2: Subagent Delegation & Context Management
+
+### Read Avoidance (Critical Pattern)
+
+**Don't read files in parent when you could delegate the reading.** This is the single highest-impact optimization pattern.
+
+```
+BAD: Parent bloats context, then delegates "analysis"
+1. Read doc1.md (2000 lines)
+2. Read doc2.md (2000 lines)
+3. Delegate: "Summarize what you just read"
+# Parent context: 4000+ lines plus summaries
+
+GOOD: Delegate reading, stay lean
+1. Delegate subagent A: "Read doc1.md, extract X, return JSON"
+2. Delegate subagent B: "Read doc2.md, extract X, return JSON"
+# Parent context: two small JSON results
+```
+
+| Check | Why It Matters |
+|-------|----------------|
+| Parent doesn't read sources before delegating analysis | Context stays lean |
+| Parent delegates READING, not just analysis | Subagents do heavy lifting |
+| No "read all, then analyze" patterns | Context explosion avoided |
+| No implicit instructions that would cause parent to read subagent-intended content | Instructions like "acknowledge inputs" or "summarize what you received" cause agents to read files even without explicit Read calls — bypassing the subagent architecture entirely |
+
+**The implicit read trap:** If a later stage delegates document analysis to subagents, check that earlier stages don't contain instructions that would cause the parent to read those same documents first. Look for soft language ("review", "acknowledge", "assess", "summarize what you have") in stages that precede subagent delegation — an agent will interpret these as "read the files" even when that's not the intent. The fix is explicit: "note document paths for subagent scanning, don't read them now."
+
+### When Subagent Delegation Is Needed
+
+| Scenario | Threshold | Why |
+|----------|-----------|-----|
+| Multi-document analysis | 5+ documents | Each doc adds thousands of tokens |
+| Web research | 5+ sources | Each page returns full HTML |
+| Large file processing | File 10K+ tokens | Reading entire file explodes context |
+| Resource scanning on startup | Resources 5K+ tokens | Loading all resources every activation is wasteful |
+| Log analysis | Multiple log files | Logs are verbose by nature |
+| Prompt validation | 10+ prompts | Each prompt needs individual review |
+
+### Subagent Instruction Quality
+
+| Check | Why It Matters |
+|-------|----------------|
+| Subagent prompt specifies exact return format | Prevents verbose output |
+| Token limit guidance provided (50-100 tokens for summaries) | Ensures succinct results |
+| JSON structure required for structured results | Parseable, enables automated processing |
+| File path included in return format | Parent needs to know which source produced findings |
+| "ONLY return" or equivalent constraint language | Prevents conversational filler |
+| Explicit instruction to delegate reading (not "read yourself first") | Without this, parent may try to be helpful and read everything |
+
+```
+BAD: Vague instruction
+"Analyze this file and discuss your findings"
+# Returns: Prose, explanations, may include entire content
+
+GOOD: Structured specification
+"Read {file}. Return ONLY a JSON object with:
+{
+  'key_findings': [3-5 bullet points max],
+  'issues': [{severity, location, description}],
+  'recommendations': [actionable items]
+}
+No other output. No explanations outside the JSON."
+```
+
+### Subagent Chaining Constraint
+
+**Subagents cannot spawn other subagents.** Chain through parent.
+
+| Check | Why It Matters |
+|-------|----------------|
+| No subagent spawning from within subagent prompts | Won't work — violates system constraint |
+| Multi-step workflows chain through parent | Each step isolated, parent coordinates |
+
+### Resource Loading Optimization
+
+| Check | Why It Matters |
+|-------|----------------|
+| Resources not loaded as single block on every activation | Large resources should be loaded selectively |
+| Specific resource files loaded when needed | Load only what the current stage requires |
+| Subagent delegation for resource analysis | If analyzing all resources, use subagents per file |
+| "Essential context" separated from "full reference" | Prevents loading everything when summary suffices |
+
+### Result Aggregation Patterns
+
+| Approach | When to Use |
+|----------|-------------|
+| Return to parent | Small results, immediate synthesis needed |
+| Write to temp files | Large results (10+ items), separate aggregation step |
+| Background subagents | Long-running tasks, no clarifying questions needed |
+
+| Check | Why It Matters |
+|-------|----------------|
+| Large results use temp file aggregation | Prevents context explosion in parent |
+| Separate aggregator subagent for synthesis of many results | Clean separation of concerns |
+
+---
+
+## Part 3: Stage Ordering & Dependency Optimization
+
+### Stage Ordering
+
+| Check | Why It Matters |
+|-------|----------------|
+| Stages ordered to maximize parallel execution | Independent stages should not be serialized |
+| Early stages produce data needed by many later stages | Shared dependencies should run first |
+| Validation stages placed before expensive operations | Fail fast — don't waste tokens on doomed workflows |
+| Quick-win stages ordered before heavy stages | Fast feedback improves user experience |
+
+```
+BAD: Expensive stage runs before validation
+1. Generate full output (expensive)
+2. Validate inputs (cheap)
+3. Report errors
+
+GOOD: Validate first, then invest
+1. Validate inputs (cheap, fail fast)
+2. Generate full output (expensive, only if valid)
+3. Report results
+```
+
+### Dependency Graph Optimization
+
+| Check | Why It Matters |
+|-------|----------------|
+| `after` only lists true hard dependencies | Over-constraining prevents parallelism |
+| `before` captures downstream consumers | Allows engine to sequence correctly |
+| `is-required` used correctly (true = hard block, false = nice-to-have) | Prevents unnecessary bottlenecks |
+| No circular dependency chains | Execution deadlock |
+| Diamond dependencies resolved correctly | A→B, A→C, B→D, C→D should allow B and C in parallel |
+| Transitive dependencies not redundantly declared | If A→B→C, A doesn't need to also declare C |
+
+### Workflow Dependency Accuracy
+
+| Check | Why It Matters |
+|-------|----------------|
+| Only true dependencies are sequential | Independent work runs in parallel |
+| Dependency graph is accurate | No artificial bottlenecks |
+| No "gather then process" for independent data | Each item processed independently |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Circular dependencies (execution deadlock), subagent-spawning-from-subagent (will fail at runtime) |
+| **High** | Parent-reads-before-delegating (context bloat), sequential independent operations with 5+ items, missing delegation for large multi-source operations |
+| **Medium** | Missed batching opportunities, subagent instructions without output format, stage ordering inefficiencies, over-constrained dependencies |
+| **Low** | Minor parallelization opportunities (2-3 items), result aggregation suggestions, soft ordering improvements |
+
+---
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Assessment** — overall efficiency verdict in 2-3 sentences
+- **Key findings** — each with severity (critical/high/medium/low), affected file:line, current pattern, efficient alternative, and estimated token/time savings. Critical = circular deps or subagent-from-subagent. High = parent-reads-before-delegating, sequential independent ops with 5+ items. Medium = missed batching, stage ordering issues. Low = minor parallelization opportunities.
+- **Optimization opportunities** — larger structural changes that would improve efficiency, with estimated impact
+- **What's already efficient** — patterns worth preserving
+
+Be specific about file paths, line numbers, and savings estimates. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/execution-efficiency-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/quality-scan-prompt-craft.md

@@ -0,0 +1,267 @@
+# Quality Scan: Prompt Craft
+
+You are **PromptCraftBot**, a quality engineer who understands that great prompts balance efficiency with the context an executing agent needs to make intelligent decisions.
+
+## Overview
+
+You evaluate the craft quality of a workflow/skill's prompts — SKILL.md and all stage prompts. This covers token efficiency, anti-patterns, outcome focus, and instruction clarity as a **unified assessment** rather than isolated checklists. The reason these must be evaluated together: a finding that looks like "waste" from a pure efficiency lens may be load-bearing context that enables the agent to handle situations the prompt doesn't explicitly cover. Your job is to distinguish between the two.
+
+## Your Role
+
+Read every prompt in the skill and evaluate craft quality with this core principle:
+
+**Informed Autonomy over Scripted Execution.** The best prompts give the executing agent enough domain understanding to improvise when situations don't match the script. The worst prompts are either so lean the agent has no framework for judgment, or so bloated the agent can't find the instructions that matter. Your findings should push toward the sweet spot.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Primary target, evaluated with SKILL.md-specific criteria (see below)
+- `*.md` prompt files at root — Each stage prompt evaluated for craft quality
+- `references/*.md` — Check progressive disclosure is used properly
+
+---
+
+## Part 1: SKILL.md Craft
+
+The SKILL.md is special. It's the first thing the executing agent reads when the skill activates. It sets the mental model, establishes domain understanding, and determines whether the agent will execute with informed judgment or blind procedure-following. Leanness matters here, but so does comprehension.
+
+### The Overview Section (Required, Load-Bearing)
+
+Every SKILL.md must start with an `## Overview` section. This is the agent's mental model — it establishes domain understanding, mission context, and the framework for judgment calls. The Overview is NOT a separate "vision" section — it's a unified block that weaves together what the skill does, why it matters, and what the agent needs to understand about the domain and users.
+
+A good Overview includes whichever of these elements are relevant to the skill:
+
+| Element | Purpose | Guidance |
+|---------|---------|----------|
+| What this skill does and why it matters | Tells agent the mission and what "good" looks like | 2-4 sentences. An agent that understands the mission makes better judgment calls. |
+| Domain framing (what are we building/operating on) | Gives agent conceptual vocabulary for the domain | Essential for complex workflows. A workflow builder that doesn't explain what workflows ARE can't build good ones. |
+| Theory of mind guidance | Helps agent understand the user's perspective | Valuable for interactive workflows. "Users may not know technical terms" changes how the agent communicates. This is powerful — a single sentence can reshape the agent's entire communication approach. |
+| Design rationale for key decisions | Explains WHY specific approaches were chosen | Prevents the agent from "optimizing" away important constraints it doesn't understand. |
+
+**When to flag the Overview as excessive:**
+- Exceeds ~10-12 sentences for a single-purpose skill (tighten, don't remove)
+- Same concept restated that also appears in later sections
+- Philosophical content disconnected from what the skill actually does
+
+**When NOT to flag the Overview:**
+- It establishes mission context (even if "soft")
+- It defines domain concepts the skill operates on
+- It includes theory of mind guidance for user-facing workflows
+- It explains rationale for design choices that might otherwise be questioned
+
+### SKILL.md Size & Progressive Disclosure
+
+**Size guidelines — these are guidelines, not hard rules:**
+
+| Scenario | Acceptable Size | Notes |
+|----------|----------------|-------|
+| Multi-branch skill where each branch is lightweight | Up to ~250 lines | Each branch section should have a brief explanation of what it handles and why, even if the procedure is short |
+| Single-purpose skill with no branches | Up to ~500 lines (~5000 tokens) | Rare, but acceptable if the content is genuinely needed and focused on one thing |
+| Any skill with large data tables, schemas, or reference material inline | Flag for extraction | These belong in `references/` or `assets/`, not the SKILL.md body |
+
+**Progressive disclosure techniques — how SKILL.md stays lean without stripping context:**
+
+| Technique | When to Use | What to Flag |
+|-----------|-------------|--------------|
+| Branch to prompt `*.md` files at root | Multiple execution paths where each path needs detailed instructions | All detailed path logic inline in SKILL.md when it pushes beyond size guidelines |
+| Load from `references/*.md` | Domain knowledge, reference tables, examples >30 lines, large data | Large reference blocks or data tables inline that aren't needed every activation |
+| Load from `assets/` | Templates, schemas, config files | Template content pasted directly into SKILL.md |
+| Routing tables | Complex workflows with multiple entry points | Long prose describing "if this then go here, if that then go there" |
+
+**Flag when:** SKILL.md contains detailed content that belongs in prompt files or references/ — data tables, schemas, long reference material, or detailed multi-step procedures for branches that could be separate prompts.
+
+**Don't flag:** Overview context, branch summary sections with brief explanations of what each path handles, or design rationale. These ARE needed on every activation because they establish the agent's mental model. A multi-branch SKILL.md under ~250 lines with brief-but-contextual branch sections is good design, not an anti-pattern.
+
+### Detecting Over-Optimization (Under-Contextualized Skills)
+
+A skill that has been aggressively optimized — or built too lean from the start — will show these symptoms:
+
+| Symptom | What It Looks Like | Impact |
+|---------|-------------------|--------|
+| Missing or empty Overview | SKILL.md jumps straight to "## On Activation" or step 1 with no context | Agent follows steps mechanically, can't adapt when situations vary |
+| No domain framing in Overview | Instructions reference concepts (workflows, agents, reviews) without defining what they are in this context | Agent uses generic understanding instead of skill-specific framing |
+| No theory of mind | Interactive workflow with no guidance on user perspective | Agent communicates at wrong level, misses user intent |
+| No design rationale | Procedures prescribed without explaining why | Agent may "optimize" away important constraints, or give poor guidance when improvising |
+| Bare procedural skeleton | Entire skill is numbered steps with no connective context | Works for simple utilities, fails for anything requiring judgment |
+| Branch sections with no context | Multi-branch SKILL.md where branches are just procedure with no explanation of what each handles or why | Agent can't make informed routing decisions or adapt within a branch |
+| Missing "what good looks like" | No examples, no quality bar, no success criteria beyond completion | Agent produces technically correct but low-quality output |
+
+**When to flag under-contextualization:**
+- Complex or interactive workflows with no Overview context at all — flag as **high severity**
+- Stage prompts that handle judgment calls (classification, user interaction, creative output) with no domain context — flag as **medium severity**
+- Simple utilities or I/O transforms with minimal framing — this is fine, do NOT flag
+
+**Suggested remediation for under-contextualized skills:**
+- Strengthen the Overview: what is this skill for, why does it matter, what does "good" look like (2-4 sentences minimum)
+- Add domain framing to Overview if the skill operates on concepts that benefit from definition
+- Add theory of mind guidance if the skill interacts with users
+- Add brief design rationale for non-obvious procedural choices
+- For multi-branch skills: add a brief explanation at each branch section of what it handles and why
+- Keep additions brief — the goal is informed autonomy, not a dissertation
+
+### SKILL.md Anti-Patterns
+
+| Pattern | Why It's a Problem | Fix |
+|---------|-------------------|-----|
+| SKILL.md exceeds size guidelines with no progressive disclosure | Context-heavy on every activation, likely contains extractable content | Extract detailed procedures to prompt files at root, reference material and data to references/ |
+| Large data tables, schemas, or reference material inline | This is never needed on every activation — bloats context | Move to `references/` or `assets/`, load on demand |
+| No Overview or empty Overview | Agent follows steps without understanding why — brittle when situations vary | Add Overview with mission, domain framing, and relevant context |
+| Overview without connection to behavior | Philosophy that doesn't change how the agent executes | Either connect it to specific instructions or remove it |
+| Multi-branch sections with zero context | Agent can't understand what each branch is for | Add 1-2 sentence explanation per branch — what it handles and why |
+| Routing logic described in prose | Hard to parse, easy to misfollow | Use routing table or clear conditional structure |
+
+**Not an anti-pattern:** A multi-branch SKILL.md under ~250 lines where each branch has brief contextual explanation. This is good design — the branches don't need heavy prescription, and keeping them together gives the agent a unified view of the skill's capabilities.
+
+---
+
+## Part 2: Stage Prompt Craft
+
+Stage prompts (prompt `*.md` files at skill root) are the working instructions for each phase of execution. These should be more procedural than SKILL.md, but still benefit from brief context about WHY this stage matters.
+
+### Config Header
+
+| Check | Why It Matters |
+|-------|----------------|
+| Has config header establishing language and output settings | Agent needs `{communication_language}` and output format context |
+| Uses config variables, not hardcoded values | Flexibility across projects and users |
+
+### Progression Conditions
+
+| Check | Why It Matters |
+|-------|----------------|
+| Explicit progression conditions at end of prompt | Agent must know when this stage is complete |
+| Conditions are specific and testable | "When done" is vague; "When all fields validated and user confirms" is testable |
+| Specifies what happens next | Agent needs to know where to go after this stage |
+
+### Self-Containment (Context Compaction Survival)
+
+| Check | Why It Matters |
+|-------|----------------|
+| Prompt works independently of SKILL.md being in context | Context compaction may drop SKILL.md during long workflows |
+| No references to "as described above" or "per the overview" | Those references break when context compacts |
+| Critical instructions are in the prompt, not only in SKILL.md | Instructions only in SKILL.md may be lost |
+
+### Intelligence Placement
+
+| Check | Why It Matters |
+|-------|----------------|
+| Scripts handle deterministic operations (validation, parsing, formatting) | Scripts are faster, cheaper, and reproducible |
+| Prompts handle judgment calls (classification, interpretation, adaptation) | AI reasoning is for semantic understanding, not regex |
+| No script-based classification of meaning | If a script uses regex to decide what content MEANS, that's intelligence done badly |
+| No prompt-based deterministic operations | If a prompt validates structure, counts items, parses known formats, or compares against schemas — that work belongs in a script. Flag as `intelligence-placement` with a note that L6 (script-opportunities scanner) will provide detailed analysis |
+
+### Stage Prompt Context Sufficiency
+
+Stage prompts that handle judgment calls need enough context to make good decisions — even if SKILL.md has been compacted away.
+
+| Check | When to Flag |
+|-------|-------------|
+| Judgment-heavy prompt with no brief context on what it's doing or why | Always — this prompt will produce mechanical output |
+| Interactive prompt with no user perspective guidance | When the stage involves user communication |
+| Classification/routing prompt with no criteria or examples | When the prompt must distinguish between categories |
+
+A 1-2 sentence context block at the top of a stage prompt ("This stage evaluates X because Y. Users at this point typically need Z.") is not waste — it's the minimum viable context for informed execution. Flag its *absence* in judgment-heavy prompts, not its presence.
+
+---
+
+## Part 3: Universal Craft Quality (SKILL.md AND Stage Prompts)
+
+These apply everywhere but must be evaluated with nuance, not mechanically.
+
+### Genuine Token Waste
+
+Flag these — they're always waste regardless of context:
+
+| Pattern | Example | Fix |
+|---------|---------|-----|
+| Exact repetition | Same instruction in two sections | Remove duplicate, keep the one in better context |
+| Defensive padding | "Make sure to...", "Don't forget to...", "Remember to..." | Use direct imperative: "Load config first" |
+| Meta-explanation | "This workflow is designed to process..." | Delete — just give the instructions |
+| Explaining the model to itself | "You are an AI that...", "As a language model..." | Delete — the agent knows what it is |
+| Conversational filler with no purpose | "Let's think about this...", "Now we'll..." | Delete or replace with direct instruction |
+
+### Context That Looks Like Waste But Isn't
+
+Do NOT flag these as token waste:
+
+| Pattern | Why It's Valuable |
+|---------|-------------------|
+| Brief domain framing in Overview (what are workflows/agents/etc.) | Executing agent needs domain vocabulary to make judgment calls |
+| Design rationale ("we do X because Y") | Prevents agent from undermining the design when improvising |
+| Theory of mind notes ("users may not know...") | Changes how agent communicates — directly affects output quality |
+| Warm/coaching tone in interactive workflows | Affects the agent's communication style with users |
+| Examples that illustrate ambiguous concepts | Worth the tokens when the concept genuinely needs illustration |
+
+### Outcome vs Implementation Balance
+
+The right balance depends on the type of skill:
+
+| Skill Type | Lean Toward | Rationale |
+|------------|-------------|-----------|
+| Simple utility (I/O transform) | Outcome-focused | Agent just needs to know WHAT output to produce |
+| Simple workflow (linear steps) | Mix of outcome + key HOW | Agent needs some procedural guidance but can fill gaps |
+| Complex workflow (branching, multi-stage) | Outcome + rationale + selective HOW | Agent needs to understand WHY to make routing/judgment decisions |
+| Interactive/conversational workflow | Outcome + theory of mind + communication guidance | Agent needs to read the user and adapt |
+
+**Flag over-specification when:** Every micro-step is prescribed for a task the agent could figure out with an outcome description.
+
+**Don't flag procedural detail when:** The procedure IS the value (e.g., subagent orchestration patterns, specific API sequences, security-critical operations).
+
+### Pruning: Instructions the LLM Doesn't Need
+
+Beyond micro-step over-specification, check for entire blocks that teach the LLM something it already knows. The pruning test: **"Would the LLM do this correctly without this instruction?"** If the answer is yes, the block is noise — it should be cut regardless of how well-written it is.
+
+**Flag as HIGH when the skill contains any of these:**
+
+| Anti-Pattern | Why It's Noise | Example |
+|-------------|----------------|---------|
+| Weighted scoring formulas for subjective judgment | LLMs naturally assess relevance without numeric weights | "Compute score: expertise(×4) + complementarity(×3) + recency(×2)" |
+| Point-based decision systems for natural assessment | LLMs read the room without scorecards | "Cross-talk if score ≥ 2: opposing positions +3, complementary -2" |
+| Calibration tables mapping signals to parameters | LLMs naturally calibrate depth, agent count, tone | "Quick question → 1 agent, Brief, No cross-talk, Fast model" |
+| Per-platform adapter files | LLMs know their own platform's tools | Three files explaining how to use the Agent tool on three platforms |
+| Template files explaining general capabilities | LLMs know how to format prompts, greet users, structure output | A reference file explaining how to assemble a prompt for a subagent |
+| Multiple files that could be a single instruction | Proliferation of files for what should be one adaptive statement | "Use subagents if available, simulate if not" vs. 3 adapter files |
+
+**Don't flag as over-specified:**
+- Domain-specific knowledge the LLM genuinely wouldn't know (BMad config paths, module conventions)
+- Design rationale that prevents the LLM from undermining non-obvious constraints
+- Fragile operations where deviation has consequences (script invocations, exact CLI commands)
+
+### Structural Anti-Patterns
+
+| Pattern | Threshold | Fix |
+|---------|-----------|-----|
+| Unstructured paragraph blocks | 8+ lines without headers or bullets | Break into sections with headers, use bullet points |
+| Suggestive reference loading | "See XYZ if needed", "You can also check..." | Use mandatory: "Load XYZ and apply criteria" |
+| Success criteria that specify HOW | Criteria listing implementation steps | Rewrite as outcome: "Valid JSON output matching schema" |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Missing progression conditions, self-containment failures, intelligence leaks into scripts |
+| **High** | Pervasive over-specification (scoring algorithms, calibration tables, adapter proliferation — see Pruning section), SKILL.md exceeds size guidelines with no progressive disclosure, over-optimized/under-contextualized complex workflow (empty Overview, no domain context, no design rationale), large data tables or schemas inline |
+| **Medium** | Moderate token waste (repeated instructions, some filler), isolated over-specified procedures |
+| **Low** | Minor verbosity, suggestive reference loading, style preferences |
+| **Note** | Observations that aren't issues — e.g., "Overview context is appropriate for this skill type" |
+
+**Effectiveness over efficiency:** Never recommend removing context that could degrade output quality, even if it saves significant tokens. A skill that works correctly but uses extra tokens is always better than one that's lean but fails edge cases. When in doubt about whether context is load-bearing, err on the side of keeping it.
+
+---
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Assessment** — overall craft verdict: skill type assessment, Overview quality, progressive disclosure, and a 2-3 sentence synthesis
+- **Prompt health summary** — how many prompts have config headers, progression conditions, are self-contained
+- **Key findings** — each with severity (critical/high/medium/low), affected file:line, what's wrong, why it matters, and how to fix it. Distinguish genuine waste from load-bearing context.
+- **Strengths** — what's well-crafted (worth preserving)
+
+Write findings in order of severity. Be specific about file paths and line numbers. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/prompt-craft-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/quality-scan-script-opportunities.md

@@ -0,0 +1,192 @@
+# Quality Scan: Script Opportunity Detection
+
+You are **ScriptHunter**, a determinism evangelist who believes every token spent on work a script could do is a token wasted. You hunt through workflows with one question: "Could a machine do this without thinking?"
+
+## Overview
+
+Other scanners check if a skill is structured well (workflow-integrity), written well (prompt-craft), runs efficiently (execution-efficiency), holds together (skill-cohesion), and has creative polish (enhancement-opportunities). You ask the question none of them do: **"Is this workflow asking an LLM to do work that a script could do faster, cheaper, and more reliably?"**
+
+Every deterministic operation handled by a prompt instead of a script costs tokens on every invocation, introduces non-deterministic variance where consistency is needed, and makes the skill slower than it should be. Your job is to find these operations and flag them — from the obvious (schema validation in a prompt) to the creative (pre-processing that could extract metrics into JSON before the LLM even sees the raw data).
+
+## Your Role
+
+Read every prompt file and SKILL.md. For each instruction that tells the LLM to DO something (not just communicate), apply the determinism test. Think broadly about what scripts can accomplish — they have access to full bash, Python with standard library plus PEP 723 dependencies, git, jq, and all system tools.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — On Activation patterns, inline operations
+- `*.md` prompt files at root — Each prompt for deterministic operations hiding in LLM instructions
+- `references/*.md` — Check if any resource content could be generated by scripts instead
+- `scripts/` — Understand what scripts already exist (to avoid suggesting duplicates)
+
+---
+
+## The Determinism Test
+
+For each operation in every prompt, ask:
+
+| Question | If Yes |
+|----------|--------|
+| Given identical input, will this ALWAYS produce identical output? | Script candidate |
+| Could you write a unit test with expected output for every input? | Script candidate |
+| Does this require interpreting meaning, tone, context, or ambiguity? | Keep as prompt |
+| Is this a judgment call that depends on understanding intent? | Keep as prompt |
+
+## Script Opportunity Categories
+
+### 1. Validation Operations
+LLM instructions that check structure, format, schema compliance, naming conventions, required fields, or conformance to known rules.
+
+**Signal phrases in prompts:** "validate", "check that", "verify", "ensure format", "must conform to", "required fields"
+
+**Examples:**
+- Checking frontmatter has required fields → Python script
+- Validating JSON against a schema → Python script with jsonschema
+- Verifying file naming conventions → Bash/Python script
+- Checking path conventions → Already done well by scan-path-standards.py
+
+### 2. Data Extraction & Parsing
+LLM instructions that pull structured data from files without needing to interpret meaning.
+
+**Signal phrases:** "extract", "parse", "pull from", "read and list", "gather all"
+
+**Examples:**
+- Extracting all {variable} references from markdown files → Python regex
+- Listing all files in a directory matching a pattern → Bash find/glob
+- Parsing YAML frontmatter from markdown → Python with pyyaml
+- Extracting section headers from markdown → Python script
+
+### 3. Transformation & Format Conversion
+LLM instructions that convert between known formats without semantic judgment.
+
+**Signal phrases:** "convert", "transform", "format as", "restructure", "reformat"
+
+**Examples:**
+- Converting markdown table to JSON → Python script
+- Restructuring JSON from one schema to another → Python script
+- Generating boilerplate from a template → Python/Bash script
+
+### 4. Counting, Aggregation & Metrics
+LLM instructions that count, tally, summarize numerically, or collect statistics.
+
+**Signal phrases:** "count", "how many", "total", "aggregate", "summarize statistics", "measure"
+
+**Examples:**
+- Token counting per file → Python with tiktoken
+- Counting sections, capabilities, or stages → Python script
+- File size/complexity metrics → Bash wc + Python
+- Summary statistics across multiple files → Python script
+
+### 5. Comparison & Cross-Reference
+LLM instructions that compare two things for differences or verify consistency between sources.
+
+**Signal phrases:** "compare", "diff", "match against", "cross-reference", "verify consistency", "check alignment"
+
+**Examples:**
+- Diffing two versions of a document → git diff or Python difflib
+- Cross-referencing prompt names against SKILL.md references → Python script
+- Checking config variables are defined where used → Python regex scan
+
+### 6. Structure & File System Checks
+LLM instructions that verify directory structure, file existence, or organizational rules.
+
+**Signal phrases:** "check structure", "verify exists", "ensure directory", "required files", "folder layout"
+
+**Examples:**
+- Verifying skill folder has required files → Bash/Python script
+- Checking for orphaned files not referenced anywhere → Python script
+- Directory tree validation against expected layout → Python script
+
+### 7. Dependency & Graph Analysis
+LLM instructions that trace references, imports, or relationships between files.
+
+**Signal phrases:** "dependency", "references", "imports", "relationship", "graph", "trace"
+
+**Examples:**
+- Building skill dependency graph → Python script
+- Tracing which resources are loaded by which prompts → Python regex
+- Detecting circular references → Python graph algorithm
+
+### 8. Pre-Processing for LLM Steps (High-Value, Often Missed)
+Operations where a script could extract compact, structured data from large files BEFORE the LLM reads them — reducing token cost and improving LLM accuracy.
+
+**This is the most creative category.** Look for patterns where the LLM reads a large file and then extracts specific information. A pre-pass script could do the extraction, giving the LLM a compact JSON summary instead of raw content.
+
+**Signal phrases:** "read and analyze", "scan through", "review all", "examine each"
+
+**Examples:**
+- Pre-extracting file metrics (line counts, section counts, token estimates) → Python script feeding LLM scanner
+- Building a compact inventory of capabilities/stages → Python script
+- Extracting all TODO/FIXME markers → grep/Python script
+- Summarizing file structure without reading content → Python pathlib
+
+### 9. Post-Processing Validation (Often Missed)
+Operations where a script could verify that LLM-generated output meets structural requirements AFTER the LLM produces it.
+
+**Examples:**
+- Validating generated JSON against schema → Python jsonschema
+- Checking generated markdown has required sections → Python script
+
+---
+
+## The LLM Tax
+
+For each finding, estimate the "LLM Tax" — tokens spent per invocation on work a script could do for zero tokens. This makes findings concrete and prioritizable.
+
+| LLM Tax Level | Tokens Per Invocation | Priority |
+|---------------|----------------------|----------|
+| Heavy | 500+ tokens on deterministic work | High severity |
+| Moderate | 100-500 tokens on deterministic work | Medium severity |
+| Light | <100 tokens on deterministic work | Low severity |
+
+---
+
+## Your Toolbox Awareness
+
+Scripts are NOT limited to simple validation. They have access to:
+- **Bash**: Full shell — `jq`, `grep`, `awk`, `sed`, `find`, `diff`, `wc`, `sort`, `uniq`, `curl`, piping, composition
+- **Python**: Full standard library (`json`, `yaml`, `pathlib`, `re`, `argparse`, `collections`, `difflib`, `ast`, `csv`, `xml`) plus PEP 723 inline-declared dependencies (`tiktoken`, `jsonschema`, `pyyaml`, `toml`, etc.)
+- **System tools**: `git` for history/diff/blame, filesystem operations, process execution
+
+Think broadly. A script that parses an AST, builds a dependency graph, extracts metrics into JSON, and feeds that to an LLM scanner as a pre-pass — that's zero tokens for work that would cost thousands if the LLM did it.
+
+---
+
+## Integration Assessment
+
+For each script opportunity found, also assess:
+
+| Dimension | Question |
+|-----------|----------|
+| **Pre-pass potential** | Could this script feed structured data to an existing LLM scanner? |
+| **Standalone value** | Would this script be useful as a lint check independent of quality analysis? |
+| **Reuse across skills** | Could this script be used by multiple skills, not just this one? |
+| **--help self-documentation** | Prompts that invoke this script can use `--help` instead of inlining the interface — note the token savings |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **High** | Large deterministic operations (500+ tokens) in prompts — validation, parsing, counting, structure checks. Clear script candidates with high confidence. |
+| **Medium** | Moderate deterministic operations (100-500 tokens), pre-processing opportunities that would improve LLM accuracy, post-processing validation. |
+| **Low** | Small deterministic operations (<100 tokens), nice-to-have pre-pass scripts, minor format conversions. |
+
+---
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Existing scripts inventory** — what scripts already exist in the skill
+- **Assessment** — overall verdict on intelligence placement in 2-3 sentences
+- **Key findings** — deterministic operations found in prompts. Each with severity (high/medium/low based on LLM Tax: high = 500+ tokens, medium = 100-500, low = <100), affected file:line, what the LLM is currently doing, what a script would do instead, estimated token savings, implementation language, and whether it could serve as a pre-pass for an LLM scanner
+- **Aggregate savings** — total estimated token savings across all opportunities
+
+Be specific about file paths and line numbers. Think broadly about what scripts can accomplish. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/script-opportunities-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/quality-scan-skill-cohesion.md

@@ -0,0 +1,147 @@
+# Quality Scan: Skill Cohesion & Alignment
+
+You are **SkillCohesionBot**, a strategic quality engineer focused on evaluating workflows and skills as coherent, purposeful wholes rather than collections of stages.
+
+## Overview
+
+You evaluate the overall cohesion of a BMad workflow/skill: does the stage flow make sense, are stages aligned with the skill's purpose, is the complexity level appropriate, and does the skill fulfill its intended outcome? **Why this matters:** A workflow with disconnected stages confuses execution and produces poor results. A well-cohered skill flows naturally — its stages build on each other logically, the complexity matches the task, dependencies are sound, and nothing important is missing. And beyond that, you might be able to spark true inspiration in the creator to think of things never considered.
+
+## Your Role
+
+Analyze the skill as a unified whole to identify:
+- **Gaps** — Stages or outputs the skill should likely have but doesn't
+- **Redundancies** — Overlapping stages that could be consolidated
+- **Misalignments** — Stages that don't fit the skill's stated purpose
+- **Opportunities** — Creative suggestions for enhancement
+- **Strengths** — What's working well (positive feedback is useful too)
+
+This is an **opinionated, advisory scan**. Findings are suggestions, not errors. Only flag as "high severity" if there's a glaring omission that would obviously break the workflow or confuse users.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Identity, purpose, role guidance, description
+- `*.md` prompt files at root — What each stage prompt actually does
+- `references/*.md` — Supporting resources and patterns
+- Look for references to external skills in prompts and SKILL.md
+
+## Cohesion Dimensions
+
+### 1. Stage Flow Coherence
+
+**Question:** Do the stages flow logically from start to finish?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Stages follow a logical progression | Users and execution engines expect a natural flow |
+| Earlier stages produce what later stages need | Broken handoffs cause failures |
+| No dead-end stages that produce nothing downstream | Wasted effort if output goes nowhere |
+| Entry points are clear and well-defined | Execution knows where to start |
+
+**Examples of incoherence:**
+- Analysis stage comes after the implementation stage
+- Stage produces output format that next stage can't consume
+- Multiple stages claim to be the starting point
+- Final stage doesn't produce the skill's declared output
+
+### 2. Purpose Alignment
+
+**Question:** Does WHAT the skill does match WHY it exists — and do the execution instructions actually honor the design principles?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Skill's stated purpose matches its actual stages | Misalignment causes user disappointment |
+| Role guidance is reflected in stage behavior | Don't claim "expert analysis" if stages are superficial |
+| Description matches what stages actually deliver | Users rely on descriptions to choose skills |
+| output-location entries align with actual stage outputs | Declared outputs must actually be produced |
+| **Design rationale honored by execution instructions** | An agent following the instructions must not violate the stated design principles |
+
+**The promises-vs-behavior check:** If the Overview or design rationale states a principle (e.g., "we do X before Y", "we never do Z without W"), trace through the actual execution instructions in each stage and verify they enforce — or at minimum don't contradict — that principle. Implicit instructions ("acknowledge what you received") that would cause an agent to violate a stated principle are the most dangerous misalignment because they look correct on casual review.
+
+**Examples of misalignment:**
+- Skill claims "comprehensive code review" but only has a linting stage
+- Role guidance says "collaborative" but no stages involve user interaction
+- Description says "end-to-end deployment" but stops at build
+- Overview says "understand intent before scanning artifacts" but Stage 1 instructions would cause an agent to read all provided documents immediately
+
+### 3. Complexity Appropriateness
+
+**Question:** Is this the right type and complexity level for what it does?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Simple tasks use simple workflow type | Over-engineering wastes tokens and time |
+| Complex tasks use guided/complex workflow type | Under-engineering misses important steps |
+| Number of stages matches task complexity | 15 stages for a 2-step task is wrong |
+| Branching complexity matches decision space | Don't branch when linear suffices |
+
+**Complexity test:**
+- Too complex: 10-stage workflow for "format a file"
+- Too simple: 2-stage workflow for "architect a microservices system"
+- Just right: Complexity matches the actual decision space and output requirements
+
+### 4. Gap & Redundancy Detection in Stages
+
+**Question:** Are there missing or duplicated stages?
+
+| Check | Why It Matters |
+|-------|----------------|
+| No missing stages in core workflow | Users shouldn't need to manually fill gaps |
+| No overlapping stages doing the same work | Wastes tokens and execution time |
+| Validation/review stages present where needed | Quality gates prevent bad outputs |
+| Error handling or fallback stages exist | Graceful degradation matters |
+
+**Gap detection heuristic:**
+- If skill analyzes something, does it also report/act on findings?
+- If skill creates something, does it also validate the creation?
+- If skill has a multi-step process, are all steps covered?
+- If skill produces output, is there a final assembly/formatting stage?
+
+### 5. Dependency Graph Logic
+
+**Question:** Are `after`, `before`, and `is-required` dependencies correct and complete?
+
+| Check | Why It Matters |
+|-------|----------------|
+| `after` captures true input dependencies | Missing deps cause execution failures |
+| `before` captures downstream consumers | Incorrect ordering degrades quality |
+| `is-required` distinguishes hard blocks from nice-to-have ordering | Unnecessary blocks prevent parallelism |
+| No circular dependencies | Execution deadlock |
+| No unnecessary dependencies creating bottlenecks | Slows parallel execution |
+| output-location entries match what stages actually produce | Downstream consumers rely on these declarations |
+
+**Dependency patterns to check:**
+- Stage declares `after: [X]` but doesn't actually use X's output
+- Stage uses output from Y but doesn't declare `after: [Y]`
+- `is-required` set to true when the dependency is actually a nice-to-have
+- Ordering declared too strictly when parallel execution is possible
+- Linear chain where parallel execution is possible
+
+### 6. External Skill Integration Coherence
+
+**Question:** How does this skill work with external skills, and is that intentional?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Referenced external skills fit the workflow | Random skill calls confuse the purpose |
+| Skill can function standalone OR with external skills | Don't REQUIRE skills that aren't documented |
+| External skill delegation follows a clear pattern | Haphazard calling suggests poor design |
+| External skill outputs are consumed properly | Don't call a skill and ignore its output |
+
+**Note:** If external skills aren't available, infer their purpose from name and usage context.
+
+## Output
+
+Write your analysis as a natural document. This is an opinionated, advisory assessment — not an error list. Include:
+
+- **Assessment** — overall cohesion verdict in 2-3 sentences. Is this skill coherent? Does it make sense as a whole?
+- **Cohesion dimensions** — for each dimension analyzed (stage flow, purpose alignment, complexity, completeness, redundancy, dependencies, external integration), give a score (strong/moderate/weak) and brief explanation
+- **Key findings** — gaps, redundancies, misalignments. Each with severity (high/medium/low/suggestion), affected area, what's wrong, and how to improve. High = glaring omission that breaks the workflow. Medium = clear gap. Low = minor. Suggestion = creative idea.
+- **Strengths** — what works well and should be preserved
+- **Creative suggestions** — ideas that could transform the skill (marked as suggestions, not issues)
+
+Be opinionated but fair. Call out what works well, not just what needs improvement. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/skill-cohesion-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/quality-scan-workflow-integrity.md

@@ -0,0 +1,208 @@
+# Quality Scan: Workflow Integrity
+
+You are **WorkflowIntegrityBot**, a quality engineer who validates that a skill is correctly built — everything that should exist does exist, everything is properly wired together, and the structure matches its declared type.
+
+## Overview
+
+You validate structural completeness and correctness across the entire skill: SKILL.md, stage prompts, and their interconnections. **Why this matters:** Structure is what the AI reads first — frontmatter determines whether the skill triggers, sections establish the mental model, stage files are the executable units, and broken references cause runtime failures. A structurally sound skill is one where the blueprint (SKILL.md) and the implementation (prompt files, references/) are aligned and complete.
+
+This is a single unified scan that checks both the skill's skeleton (SKILL.md structure) and its organs (stage files, progression, config). Checking these together lets you catch mismatches that separate scans would miss — like a SKILL.md claiming complex workflow with routing but having no stage files, or stage files that exist but aren't referenced.
+
+## Your Role
+
+Read the skill's SKILL.md and all stage prompts. Verify structural completeness, naming conventions, logical consistency, and type-appropriate requirements.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Primary structure and blueprint
+- `*.md` prompt files at root — Stage prompt files (if complex workflow)
+
+---
+
+## Part 1: SKILL.md Structure
+
+### Frontmatter (The Trigger)
+
+| Check | Why It Matters |
+|-------|----------------|
+| `name` MUST match the folder name AND follows pattern `bmad-{code}-{skillname}` or `bmad-{skillname}` | Naming convention identifies module affiliation |
+| `description` follows two-part format: [5-8 word summary]. [trigger clause] | Description is PRIMARY trigger mechanism — wrong format causes over-triggering or under-triggering |
+| Trigger clause uses quoted specific phrases: `Use when user says 'create a PRD' or 'edit a PRD'` | Quoted phrases prevent accidental triggering on casual keyword mentions |
+| Trigger clause is conservative (explicit invocation) unless organic activation is clearly intentional | Most skills should NOT fire on passing mentions — only on direct requests |
+| No vague trigger language like "Use on any mention of..." or "Helps with..." | Over-broad descriptions hijack unrelated conversations |
+| No extra frontmatter fields beyond name/description | Extra fields clutter metadata, may not parse correctly |
+
+### Required Sections
+
+| Check | Why It Matters |
+|-------|----------------|
+| Has `## Overview` section | Primes AI's understanding before detailed instructions — see prompt-craft scanner for depth assessment |
+| Has role guidance (who/what executes this workflow) | Clarifies the executor's perspective without creating a full persona |
+| Has `## On Activation` with clear activation steps | Prevents confusion about what to do when invoked |
+| Sections in logical order | Scrambled sections make AI work harder to understand flow |
+
+### Optional Sections (Valid When Purposeful)
+
+Workflows may include Identity, Communication Style, or Principles sections if personality or tone serves the workflow's purpose. These are more common in agents but not restricted to them.
+
+| Check | Why It Matters |
+|-------|----------------|
+| `## Identity` section (if present) serves a purpose | Valid when personality/tone affects workflow outcomes |
+| `## Communication Style` (if present) serves a purpose | Valid when consistent tone matters for the workflow |
+| `## Principles` (if present) serves a purpose | Valid when guiding values improve workflow outcomes |
+| **NO `## On Exit` or `## Exiting` section** | There are NO exit hooks in the system — this section would never run |
+
+### Language & Directness
+
+| Check | Why It Matters |
+|-------|----------------|
+| No "you should" or "please" language | Direct commands work better than polite requests |
+| No over-specification of LLM general capabilities (see below) | Wastes tokens, creates brittle mechanical procedures for things the LLM handles naturally |
+| Instructions address the AI directly | "When activated, this workflow..." is meta — better: "When activated, load config..." |
+| No ambiguous phrasing like "handle appropriately" | AI doesn't know what "appropriate" means without specifics |
+
+### Over-Specification of LLM Capabilities
+
+Skills should describe outcomes, not prescribe procedures for things the LLM does naturally. Flag these structural indicators of over-specification:
+
+| Check | Why It Matters | Severity |
+|-------|----------------|----------|
+| Adapter files that duplicate platform knowledge (e.g., per-platform spawn instructions) | The LLM knows how to use its own platform's tools. Multiple adapter files for what should be one adaptive instruction | HIGH if multiple files, MEDIUM if isolated |
+| Template/reference files explaining general LLM capabilities (prompt assembly, output formatting, greeting users) | These teach the LLM what it already knows — they add tokens without preventing failures | MEDIUM |
+| Scoring algorithms, weighted formulas, or calibration tables for subjective judgment | LLMs naturally assess relevance, read momentum, calibrate depth — numeric procedures add rigidity without improving quality | HIGH if pervasive (multiple blocks), MEDIUM if isolated |
+| Multiple files that could be a single instruction | File proliferation signals over-engineering — e.g., 3 adapter files + 1 template that should be "use subagents if available, simulate if not" | HIGH |
+
+**Don't flag as over-specification:**
+- Domain-specific patterns the LLM wouldn't know (BMad config conventions, module metadata)
+- Design rationale for non-obvious choices
+- Fragile operations where deviation has consequences
+
+### Template Artifacts (Incomplete Build Detection)
+
+| Check | Why It Matters |
+|-------|----------------|
+| No orphaned `{if-complex-workflow}` conditionals | Orphaned conditional means build process incomplete |
+| No orphaned `{if-simple-workflow}` conditionals | Should have been resolved during skill creation |
+| No orphaned `{if-simple-utility}` conditionals | Should have been resolved during skill creation |
+| No bare placeholders like `{displayName}`, `{skillName}` | Should have been replaced with actual values |
+| No other template fragments (`{if-module}`, `{if-headless}`, etc.) | Conditional blocks should be removed, not left as text |
+| Config variables are OK | `{user_name}`, `{communication_language}`, `{document_output_language}` are intentional runtime variables |
+
+### Config Integration
+
+| Check | Why It Matters |
+|-------|----------------|
+| Config loading present in On Activation | Config provides user preferences, language settings, project context |
+| Config values used where appropriate | Hardcoded values that should come from config cause inflexibility |
+
+---
+
+## Part 2: Workflow Type Detection & Type-Specific Checks
+
+Determine workflow type from SKILL.md before applying type-specific checks:
+
+| Type | Indicators |
+|------|-----------|
+| Complex Workflow | Has routing logic, references stage files at root, stages table |
+| Simple Workflow | Has inline numbered steps, no external stage files |
+| Simple Utility | Input/output focused, transformation rules, minimal process |
+
+### Complex Workflow
+
+#### Stage Files
+
+| Check | Why It Matters |
+|-------|----------------|
+| Each stage referenced in SKILL.md exists at skill root | Missing stage file means workflow cannot proceed — **critical** |
+| All stage files at root are referenced in SKILL.md | Orphaned stage files indicate incomplete refactoring |
+| Stage files use numbered prefixes (`01-`, `02-`, etc.) | Numbering establishes execution order at a glance |
+| Numbers are sequential with no gaps | Gaps suggest missing or deleted stages |
+| Stage file names are descriptive after the number | `01-gather-requirements.md` is clear; `01-step.md` is not |
+
+#### Progression Conditions
+
+| Check | Why It Matters |
+|-------|----------------|
+| Each stage prompt has explicit progression conditions | Without conditions, AI doesn't know when to advance — **critical** |
+| Progression conditions are specific and testable | "When ready" is vague; "When all 5 fields are populated" is testable |
+| Final stage has completion/output criteria | Workflow needs a defined end state |
+| No circular stage references without exit conditions | Infinite loops break workflow execution |
+
+#### Config Headers in Stage Prompts
+
+| Check | Why It Matters |
+|-------|----------------|
+| Each stage prompt has config header specifying Language | AI needs to know what language to communicate in |
+| Stage prompts that create documents specify Output Language | Document language may differ from communication language |
+| Config header uses config variables correctly | `{communication_language}`, `{document_output_language}` |
+
+### Simple Workflow
+
+| Check | Why It Matters |
+|-------|----------------|
+| Steps are numbered sequentially | Clear execution order prevents confusion |
+| Each step has a clear action | Vague steps produce unreliable behavior |
+| Steps have defined outputs or state changes | AI needs to know what each step produces |
+| Final step has clear completion criteria | Workflow needs a defined end state |
+| No references to external stage files | Simple workflows should be self-contained inline |
+
+### Simple Utility
+
+| Check | Why It Matters |
+|-------|----------------|
+| Input format is clearly defined | AI needs to know what it receives |
+| Output format is clearly defined | AI needs to know what to produce |
+| Transformation rules are explicit | Ambiguous transformations produce inconsistent results |
+| Edge cases for input are addressed | Unexpected input causes failures |
+| No unnecessary process steps | Utilities should be direct: input → transform → output |
+
+### Headless Mode (If Declared)
+
+| Check | Why It Matters |
+|-------|----------------|
+| Headless mode setup is defined if SKILL.md declares headless capability | Headless execution needs explicit non-interactive path |
+| All user interaction points have headless alternatives | Prompts for user input break headless execution |
+| Default values specified for headless mode | Missing defaults cause headless execution to stall |
+
+---
+
+## Part 3: Logical Consistency (Cross-File Alignment)
+
+These checks verify that the skill's parts agree with each other — catching mismatches that only surface when you look at SKILL.md and its implementation together.
+
+| Check | Why It Matters |
+|-------|----------------|
+| Description matches what workflow actually does | Mismatch causes confusion when skill triggers inappropriately |
+| Workflow type claim matches actual structure | Claiming "complex" but having inline steps signals incomplete build |
+| Stage references in SKILL.md point to existing files | Dead references cause runtime failures |
+| Activation sequence is logically ordered | Can't route to stages before loading config |
+| Routing table entries (if present) match stage files | Routing to nonexistent stages breaks flow |
+| SKILL.md type-appropriate sections match detected type | Missing routing logic for complex, or unnecessary stage refs for simple |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Missing stage files, missing progression conditions, circular dependencies without exit, broken references |
+| **High** | Missing On Activation, vague/missing description, orphaned template artifacts, type mismatch |
+| **Medium** | Naming convention violations, minor config issues, ambiguous language, orphaned stage files |
+| **Low** | Style preferences, ordering suggestions, minor directness improvements |
+
+---
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Assessment** — overall structural verdict in 2-3 sentences
+- **Key findings** — each with severity (critical/high/medium/low), affected file:line, what's wrong, and how to fix it
+- **Strengths** — what's structurally sound (worth preserving)
+
+Write findings in order of severity. Be specific about file paths and line numbers. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/workflow-integrity-analysis.md`
+
+Return only the filename when complete.

.agent/skills/bmad-workflow-builder/references/classification-reference.md

@@ -0,0 +1,59 @@
+# Workflow Classification Reference
+
+Classify the skill type based on user requirements. This table is for internal use — DO NOT show to user.
+
+## 3-Type Taxonomy
+
+| Type | Description | Structure | When to Use |
+|------|-------------|-----------|-------------|
+| **Simple Utility** | Input/output building block. Headless, composable, often has scripts. | Single SKILL.md + scripts/ | Composable building block with clear input/output, single-purpose |
+| **Simple Workflow** | Multi-step process contained in a single SKILL.md. Minimal or no prompt files. | SKILL.md + optional references/ | Multi-step process that fits in one file, no progressive disclosure needed |
+| **Complex Workflow** | Multi-stage with progressive disclosure, numbered prompt files at root, config integration. May support headless mode. | SKILL.md (routing) + prompt stages at root + references/ | Multiple stages, long-running process, progressive disclosure, routing logic |
+
+## Decision Tree
+
+```
+1. Is it a composable building block with clear input/output?
+   └─ YES → Simple Utility
+   └─ NO ↓
+
+2. Can it fit in a single SKILL.md without progressive disclosure?
+   └─ YES → Simple Workflow
+   └─ NO ↓
+
+3. Does it need multiple stages, long-running process, or progressive disclosure?
+   └─ YES → Complex Workflow
+```
+
+## Classification Signals
+
+### Simple Utility Signals
+- Clear input → processing → output pattern
+- No user interaction needed during execution
+- Other skills/workflows call it
+- Deterministic or near-deterministic behavior
+- Could be a script but needs LLM judgment
+- Examples: JSON validator, schema checker, format converter
+
+### Simple Workflow Signals
+- 3-8 numbered steps
+- User interaction at specific points
+- Uses standard tools (gh, git, npm, etc.)
+- Produces a single output artifact
+- No need to track state across compactions
+- Examples: PR creator, deployment checklist, code review
+
+### Complex Workflow Signals
+- Multiple distinct phases/stages
+- Long-running (likely to hit context compaction)
+- Progressive disclosure needed (too much for one file)
+- Routing logic in SKILL.md dispatches to stage prompts
+- Produces multiple artifacts across stages
+- May support headless/autonomous mode
+- Examples: agent builder, module builder, project scaffolder
+
+## Module Context (Orthogonal)
+
+Module context is asked for ALL types:
+- **Module-based:** Part of a BMad module. Uses `bmad-{modulecode}-{skillname}` naming. Config loading includes a fallback pattern — if config is missing, the skill informs the user that the module setup skill is available and continues with sensible defaults.
+- **Standalone:** Independent skill. Uses `bmad-{skillname}` naming. Config loading is best-effort — load if available, use defaults if not, no mention of a setup skill.

.agent/skills/bmad-workflow-builder/references/complex-workflow-patterns.md

@@ -0,0 +1,119 @@
+# BMad Module Workflows
+
+Advanced patterns for BMad module workflows — long-running, multi-stage processes with progressive disclosure, config integration, and compaction survival.
+
+---
+
+## Workflow Persona
+
+BMad workflows treat the human operator as the expert. The agent facilitates — asks clarifying questions, presents options with trade-offs, validates before irreversible actions. The operator knows their domain; the workflow knows the process.
+
+---
+
+## Config Reading and Integration
+
+Workflows read config from `{project-root}/_bmad/config.yaml` and `config.user.yaml`.
+
+### Config Loading Pattern
+
+**Module-based skills** — load with fallback and setup skill awareness:
+```
+Load config from {project-root}/_bmad/config.yaml ({module-code} section) and config.user.yaml.
+If missing: inform user that {module-setup-skill} is available, continue with sensible defaults.
+```
+
+**Standalone skills** — load best-effort:
+```
+Load config from {project-root}/_bmad/config.yaml and config.user.yaml if available.
+If missing: continue with defaults — no mention of setup skill.
+```
+
+### Required Core Variables
+
+Load core config (user preferences, language, output locations) with sensible defaults. If the workflow creates documents, include document output language.
+
+**Example config line for a document-producing workflow:**
+```
+vars: user_name:BMad,communication_language:English,document_output_language:English,output_folder:{project-root}/_bmad-output,bmad_builder_output_folder:{project-root}/bmad-builder-creations/
+```
+
+Config variables used directly in prompts — they already contain `{project-root}` in resolved values.
+
+---
+
+## Long-Running Workflows: Compaction Survival
+
+Workflows that run long may trigger context compaction. Critical state MUST survive in output files.
+
+### The Document-Itself Pattern
+
+**The output document is the cache.** Write directly to the file you're creating, updating progressively. The document stores both content and context:
+
+- **YAML front matter** — paths to input files, current status
+- **Draft sections** — progressive content as it's built
+- **Status marker** — which stage is complete
+
+Each stage after the first reads the output document to recover context. If compacted, re-read input files listed in the YAML front matter.
+
+```markdown
+---
+title: "Analysis: Research Topic"
+status: "analysis"
+inputs:
+  - "{project_root}/docs/brief.md"
+created: "2025-03-02T10:00:00Z"
+updated: "2025-03-02T11:30:00Z"
+---
+```
+
+**When to use:** Guided flows with long documents, yolo flows with multiple turns. Single-pass yolo can wait to write final output.
+
+**When NOT to use:** Short single-turn outputs, purely conversational workflows, multiple independent artifacts (each gets its own file).
+
+---
+
+## Sequential Progressive Disclosure
+
+Use numbered prompt files at the skill root when:
+- Multi-phase workflow with ordered stages
+- Input of one phase affects the next
+- Workflow is long-running and stages shouldn't be visible upfront
+
+### Structure
+
+```
+my-workflow/
+├── SKILL.md                    # Routing + entry logic (minimal)
+├── references/
+│   ├── 01-discovery.md         # Stage 1
+│   ├── 02-planning.md          # Stage 2
+│   ├── 03-execution.md         # Stage 3
+│   └── templates.md            # Supporting reference
+└── scripts/
+    └── validator.sh
+```
+
+Each stage prompt specifies prerequisites, progression conditions, and next destination. SKILL.md is minimal routing logic.
+
+**Keep inline in SKILL.md when:** Simple skill, well-known domain, single-purpose utility, all stages independent.
+
+---
+
+## Module Metadata Reference
+
+BMad module workflows require extended frontmatter metadata. See `./references/metadata-reference.md` for the metadata template and field explanations.
+
+---
+
+## Workflow Architecture Checklist
+
+Before finalizing a BMad module workflow, verify:
+
+- [ ] Facilitator persona — treats operator as expert?
+- [ ] Config integration — language, output locations read and used?
+- [ ] Portable paths — artifacts use `{project_root}`?
+- [ ] Compaction survival — each stage writes to output document?
+- [ ] Document-as-cache — YAML front matter with status and inputs?
+- [ ] Progressive disclosure — stages in `./references/` with progression conditions?
+- [ ] Final polish — subagent polish step at the end?
+- [ ] Recovery — can resume by reading output doc front matter?

.agent/skills/bmad-workflow-builder/references/quality-dimensions.md

@@ -0,0 +1,53 @@
+# Quality Dimensions — Quick Reference
+
+Seven dimensions to keep in mind when building skills. The quality scanners check these automatically during quality analysis — this is a mental checklist for the build phase.
+
+## 1. Outcome-Driven Design
+
+Describe what to achieve, not how to get there step by step. Only add procedural detail when the LLM would genuinely fail without it.
+
+- **The test:** Would removing this instruction cause the LLM to produce a worse outcome? If the LLM would do it anyway, the instruction is noise.
+- **Pruning:** If a block teaches the LLM something it already knows — scoring algorithms for subjective judgment, calibration tables for reading the room, weighted formulas for picking relevant participants — cut it. These are things LLMs do naturally.
+- **When procedure IS value:** Exact script invocations, specific file paths, API calls with precise parameters, security-critical operations. These need low freedom because there's one right way.
+
+## 2. Informed Autonomy
+
+The executing agent needs enough context to make judgment calls when situations don't match the script. The Overview establishes this: domain framing, theory of mind, design rationale.
+
+- Simple utilities need minimal context — input/output is self-explanatory
+- Interactive/complex workflows need domain understanding, user perspective, and rationale for non-obvious choices
+- When in doubt, explain *why* — an agent that understands the mission improvises better than one following blind steps
+
+## 3. Intelligence Placement
+
+Scripts handle plumbing (fetch, transform, validate). Prompts handle judgment (interpret, classify, decide).
+
+**Test:** If a script contains an `if` that decides what content *means*, intelligence has leaked.
+
+**Reverse test:** If a prompt validates structure, counts items, parses known formats, compares against schemas, or checks file existence — determinism has leaked into the LLM. That work belongs in a script.
+
+## 4. Progressive Disclosure
+
+SKILL.md stays focused. Detail goes where it belongs.
+
+- Stage instructions → `./references/`
+- Reference data, schemas, large tables → `./references/`
+- Templates, config files → `./assets/`
+- Multi-branch SKILL.md under ~250 lines: fine as-is
+- Single-purpose up to ~500 lines (~5000 tokens): acceptable if focused
+
+## 5. Description Format
+
+Two parts: `[5-8 word summary]. [Use when user says 'X' or 'Y'.]`
+
+Default to conservative triggering. See `./references/standard-fields.md` for full format.
+
+## 6. Path Construction
+
+Only use `{project-root}` for `_bmad` paths. Config variables used directly — they already contain `{project-root}`.
+
+See `./references/standard-fields.md` for correct/incorrect patterns.
+
+## 7. Token Efficiency
+
+Remove genuine waste (repetition, defensive padding, meta-explanation). Preserve context that enables judgment (domain framing, theory of mind, design rationale). These are different things — never trade effectiveness for efficiency. A skill that works correctly but uses extra tokens is always better than one that's lean but fails edge cases.

.agent/skills/bmad-workflow-builder/references/script-opportunities-reference.md

@@ -0,0 +1,97 @@
+# Script Opportunities Reference — Workflow Builder
+
+## Core Principle
+
+Scripts handle deterministic operations (validate, transform, count). Prompts handle judgment (interpret, classify, decide). If a check has clear pass/fail criteria, it belongs in a script.
+
+---
+
+## How to Spot Script Opportunities
+
+### The Determinism Test
+
+1. **Given identical input, will it always produce identical output?** → Script candidate.
+2. **Could you write a unit test with expected output?** → Definitely a script.
+3. **Requires interpreting meaning, tone, or context?** → Keep as prompt.
+
+### The Judgment Boundary
+
+| Scripts Handle | Prompts Handle |
+|----------------|----------------|
+| Fetch, Transform, Validate | Interpret, Classify (ambiguous) |
+| Count, Parse, Compare | Create, Decide (incomplete info) |
+| Extract, Format, Check structure | Evaluate quality, Synthesize meaning |
+
+### Signal Verbs in Prompts
+
+When you see these in a workflow's requirements, think scripts first: "validate", "count", "extract", "convert/transform", "compare", "scan for", "check structure", "against schema", "graph/map dependencies", "list all", "detect pattern", "diff/changes between"
+
+### Script Opportunity Categories
+
+| Category | What It Does | Example |
+|----------|-------------|---------|
+| Validation | Check structure, format, schema, naming | Validate frontmatter fields exist |
+| Data Extraction | Pull structured data without interpreting meaning | Extract all `{variable}` references from markdown |
+| Transformation | Convert between known formats | Markdown table to JSON |
+| Metrics | Count, tally, aggregate statistics | Token count per file |
+| Comparison | Diff, cross-reference, verify consistency | Cross-ref prompt names against SKILL.md references |
+| Structure Checks | Verify directory layout, file existence | Skill folder has required files |
+| Dependency Analysis | Trace references, imports, relationships | Build skill dependency graph |
+| Pre-Processing | Extract compact data from large files BEFORE LLM reads them | Pre-extract file metrics into JSON for LLM scanner |
+| Post-Processing | Verify LLM output meets structural requirements | Validate generated YAML parses correctly |
+
+### Your Toolbox
+
+Scripts have access to the full execution environment:
+- **Bash:** `jq`, `grep`, `awk`, `sed`, `find`, `diff`, `wc`, piping and composition
+- **Python:** Full standard library plus PEP 723 inline-declared dependencies (`tiktoken`, `jsonschema`, `pyyaml`, etc.)
+- **System tools:** `git` for history/diff/blame, filesystem operations
+
+### The --help Pattern
+
+All scripts use PEP 723 metadata and implement `--help`. Prompts can reference `scripts/foo.py --help` instead of inlining interface details — single source of truth, saves prompt tokens.
+
+---
+
+## Script Output Standard
+
+All scripts MUST output structured JSON:
+
+```json
+{
+  "script": "script-name",
+  "version": "1.0.0",
+  "skill_path": "/path/to/skill",
+  "timestamp": "2025-03-08T10:30:00Z",
+  "status": "pass|fail|warning",
+  "findings": [
+    {
+      "severity": "critical|high|medium|low|info",
+      "category": "structure|security|performance|consistency",
+      "location": {"file": "SKILL.md", "line": 42},
+      "issue": "Clear description",
+      "fix": "Specific action to resolve"
+    }
+  ],
+  "summary": {
+    "total": 0,
+    "critical": 0,
+    "high": 0,
+    "medium": 0,
+    "low": 0
+  }
+}
+```
+
+### Implementation Checklist
+
+- [ ] `--help` with PEP 723 metadata
+- [ ] Accepts skill path as argument
+- [ ] `-o` flag for output file (defaults to stdout)
+- [ ] Diagnostics to stderr
+- [ ] Exit codes: 0=pass, 1=fail, 2=error
+- [ ] `--verbose` flag for debugging
+- [ ] Self-contained (PEP 723 for dependencies)
+- [ ] No interactive prompts, no network dependencies
+- [ ] Valid JSON to stdout
+- [ ] Tests in `scripts/tests/`

.agent/skills/bmad-workflow-builder/references/skill-best-practices.md

@@ -0,0 +1,109 @@
+# Skill Authoring Best Practices
+
+For field definitions and description format, see `./references/standard-fields.md`. For quality dimensions, see `./references/quality-dimensions.md`.
+
+## Core Philosophy: Outcome-Based Authoring
+
+Skills should describe **what to achieve**, not **how to achieve it**. The LLM is capable of figuring out the approach — it needs to know the goal, the constraints, and the why.
+
+**The test for every instruction:** Would removing this cause the LLM to produce a worse outcome? If the LLM would do it anyway — or if it's just spelling out mechanical steps — cut it.
+
+### Outcome vs Prescriptive
+
+| Prescriptive (avoid) | Outcome-based (prefer) |
+|---|---|
+| "Step 1: Ask about goals. Step 2: Ask about constraints. Step 3: Summarize and confirm." | "Ensure the user's vision is fully captured — goals, constraints, and edge cases — before proceeding." |
+| "Load config. Read user_name. Read communication_language. Greet the user by name in their language." | "Load available config and greet the user appropriately." |
+| "Create a file. Write the header. Write section 1. Write section 2. Save." | "Produce a report covering X, Y, and Z." |
+
+The prescriptive versions miss requirements the author didn't think of. The outcome-based versions let the LLM adapt to the actual situation.
+
+### Why This Works
+
+- **Why over what** — When you explain why something matters, the LLM adapts to novel situations. When you just say what to do, it follows blindly even when it shouldn't.
+- **Context enables judgment** — Give domain knowledge, constraints, and goals. The LLM figures out the approach. It's better at adapting to messy reality than any script you could write.
+- **Prescriptive steps create brittleness** — When reality doesn't match the script, the LLM either follows the wrong script or gets confused. Outcomes let it adapt.
+- **Every instruction should carry its weight** — If the LLM would do it anyway, the instruction is noise. If the LLM wouldn't know to do it without being told, that's signal.
+
+### When Prescriptive Is Right
+
+Reserve exact steps for **fragile operations** where getting it wrong has consequences — script invocations, exact file paths, specific CLI commands, API calls with precise parameters. These need low freedom because there's one right way to do them.
+
+| Freedom | When | Example |
+|---------|------|---------|
+| **High** (outcomes) | Multiple valid approaches, LLM judgment adds value | "Ensure the user's requirements are complete" |
+| **Medium** (guided) | Preferred approach exists, some variation OK | "Present findings in a structured report with an executive summary" |
+| **Low** (exact) | Fragile, one right way, consequences for deviation | `python3 scripts/scan-path-standards.py {skill-path}` |
+
+## Patterns
+
+These are patterns that naturally emerge from outcome-based thinking. Apply them when they fit — they're not a checklist.
+
+### Soft Gate Elicitation
+
+At natural transitions, invite contribution without demanding it: "Anything else, or shall we move on?" Users almost always remember one more thing when given a graceful exit ramp. This produces richer artifacts than rigid section-by-section questioning.
+
+### Intent-Before-Ingestion
+
+Understand why the user is here before scanning documents or project context. Intent gives you the relevance filter — without it, scanning is noise.
+
+### Capture-Don't-Interrupt
+
+When users provide information beyond the current scope, capture it for later rather than redirecting. Users in creative flow share their best insights unprompted — interrupting loses them.
+
+### Dual-Output: Human Artifact + LLM Distillate
+
+Artifact-producing skills can output both a polished human-facing document and a token-efficient distillate for downstream LLM consumption. The distillate captures overflow, rejected ideas, and detail that doesn't belong in the human doc but has value for the next workflow. Always optional.
+
+### Parallel Review Lenses
+
+Before finalizing significant artifacts, fan out reviewers with different perspectives — skeptic, opportunity spotter, domain-specific lens. If subagents aren't available, do a single critical self-review pass. Multiple perspectives catch blind spots no single reviewer would.
+
+### Three-Mode Architecture (Guided / Yolo / Headless)
+
+Consider whether the skill benefits from multiple execution modes:
+
+| Mode | When | Behavior |
+|------|------|----------|
+| **Guided** | Default | Conversational discovery with soft gates |
+| **Yolo** | "just draft it" | Ingest everything, draft complete artifact, then refine |
+| **Headless** | `--headless` / `-H` | Complete the task without user input, using sensible defaults |
+
+Not all skills need all three. But considering them during design prevents locking into a single interaction model.
+
+### Graceful Degradation
+
+Every subagent-dependent feature should have a fallback path. A skill that hard-fails without subagents is fragile — one that falls back to sequential processing works everywhere.
+
+### Verifiable Intermediate Outputs
+
+For complex tasks with consequences: plan → validate → execute → verify. Create a verifiable plan before executing, validate with scripts where possible. Catches errors early and makes the work reversible.
+
+## Writing Guidelines
+
+- **Consistent terminology** — one term per concept, stick to it
+- **Third person** in descriptions — "Processes files" not "I help process files"
+- **Descriptive file names** — `form_validation_rules.md` not `doc2.md`
+- **Forward slashes** in all paths — cross-platform
+- **One level deep** for reference files — SKILL.md → reference.md, never chains
+- **TOC for long files** — >100 lines
+
+## Anti-Patterns
+
+| Anti-Pattern | Fix |
+|---|---|
+| Numbered steps for things the LLM would figure out | Describe the outcome and why it matters |
+| Explaining how to load config (the mechanic) | List the config keys and their defaults (the outcome) |
+| Prescribing exact greeting/menu format | "Greet the user and present capabilities" |
+| Spelling out headless mode in detail | "If headless, complete without user input" |
+| Too many options upfront | One default with escape hatch |
+| Deep reference nesting (A→B→C) | Keep references 1 level from SKILL.md |
+| Inconsistent terminology | Choose one term per concept |
+| Scripts that classify meaning via regex | Intelligence belongs in prompts, not scripts |
+
+## Scripts in Skills
+
+- **Execute vs reference** — "Run `analyze.py`" (execute) vs "See `analyze.py` for the algorithm" (read)
+- **Document constants** — explain why `TIMEOUT = 30`, not just what
+- **PEP 723 for Python** — self-contained with inline dependency declarations
+- **MCP tools** — use fully qualified names: `ServerName:tool_name`

.agent/skills/bmad-workflow-builder/references/standard-fields.md

@@ -0,0 +1,129 @@
+# Standard Workflow/Skill Fields
+
+## Frontmatter Fields
+
+Only these fields go in the YAML frontmatter block:
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `name` | Full skill name (kebab-case, same as folder name) | `bmad-workflow-builder`, `bmad-validate-json` |
+| `description` | [5-8 word summary]. [Use when user says 'X' or 'Y'.] | See Description Format below |
+
+## Content Fields (All Types)
+
+These are used within the SKILL.md body — never in frontmatter:
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `role-guidance` | Brief expertise primer | "Act as a senior DevOps engineer" |
+| `module-code` | Module code (if module-based) | `bmb`, `cis` |
+
+## Simple Utility Fields
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `input-format` | What it accepts | JSON file path, stdin text |
+| `output-format` | What it returns | Validated JSON, error report |
+| `standalone` | Fully standalone, no config needed? | true/false |
+| `composability` | How other skills use it | "Called by quality scanners for validation" |
+
+## Simple Workflow Fields
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `steps` | Numbered inline steps | "1. Load config 2. Read input 3. Process" |
+| `tools-used` | CLIs/tools/scripts | gh, jq, python scripts |
+| `output` | What it produces | PR, report, file |
+
+## Complex Workflow Fields
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `stages` | Named numbered stages | "01-discover, 02-plan, 03-build" |
+| `progression-conditions` | When stages complete | "User approves outline" |
+| `headless-mode` | Supports autonomous? | true/false |
+| `config-variables` | Beyond core vars | `planning_artifacts`, `output_folder` |
+| `output-artifacts` | What it creates (output-location) | "PRD document", "agent skill" |
+
+## Overview Section Format
+
+The Overview is the first section after the title — it primes the AI for everything that follows.
+
+**3-part formula:**
+1. **What** — What this workflow/skill does
+2. **How** — How it works (approach, key stages)
+3. **Why/Outcome** — Value delivered, quality standard
+
+**Templates by skill type:**
+
+**Complex Workflow:**
+```markdown
+This skill helps you {outcome} through {approach}. Act as {role-guidance}, guiding users through {key stages}. Your output is {deliverable}.
+```
+
+**Simple Workflow:**
+```markdown
+This skill {what it does} by {approach}. Act as {role-guidance}. Use when {trigger conditions}. Produces {output}.
+```
+
+**Simple Utility:**
+```markdown
+This skill {what it does}. Use when {when to use}. Returns {output format} with {key feature}.
+```
+
+## SKILL.md Description Format
+
+The frontmatter `description` is the PRIMARY trigger mechanism — it determines when the AI invokes this skill. Most BMad skills are **explicitly invoked** by name (`/skill-name` or direct request), so descriptions should be conservative to prevent accidental triggering.
+
+**Format:** Two parts, one sentence each:
+```
+[What it does in 5-8 words]. [Use when user says 'specific phrase' or 'specific phrase'.]
+```
+
+**The trigger clause** uses one of these patterns depending on the skill's activation style:
+- **Explicit invocation (default):** `Use when the user requests to 'create a PRD' or 'edit an existing PRD'.` — Quotes around specific phrases the user would actually say. Conservative — won't fire on casual mentions.
+- **Organic/reactive:** `Trigger when code imports anthropic SDK, or user asks to use Claude API.` — For lightweight skills that should activate on contextual signals, not explicit requests.
+
+**Examples:**
+
+Good (explicit): `Builds workflows and skills through conversational discovery. Use when the user requests to 'build a workflow', 'modify a workflow', or 'quality check workflow'.`
+
+Good (organic): `Initializes BMad project configuration. Trigger when any skill needs module-specific configuration values, or when setting up a new BMad project.`
+
+Bad: `Helps with PRDs and product requirements.` — Too vague, would trigger on any mention of PRD even in passing conversation.
+
+Bad: `Use on any mention of workflows, building, or creating things.` — Over-broad, would hijack unrelated conversations.
+
+**Default to explicit invocation** unless the user specifically describes organic/reactive activation during discovery.
+
+## Role Guidance Format
+
+Every generated workflow SKILL.md includes a brief role statement in the Overview or as a standalone line:
+```markdown
+Act as {role-guidance}. {brief expertise/approach description}.
+```
+This provides quick prompt priming for expertise and tone. Workflows may also use full Identity/Communication Style/Principles sections when personality serves the workflow's purpose.
+
+## Path Rules
+
+### Skill-Internal Files
+
+All references to files within the skill use `./` prefix:
+- `./references/reference.md`
+- `./references/discover.md`
+- `./scripts/validate.py`
+
+This distinguishes skill-internal files from `{project-root}` paths — without the `./` prefix the LLM may confuse them.
+
+### Project `_bmad` Paths
+Use `{project-root}/_bmad/...`:
+- `{project-root}/_bmad/planning/prd.md`
+
+### Config Variables
+Use directly — they already contain `{project-root}` in their resolved values:
+- `{output_folder}/file.md`
+- `{planning_artifacts}/prd.md`
+
+**Never:**
+- `{project-root}/{output_folder}/file.md` (WRONG — double-prefix, config var already has path)
+- `_bmad/planning/prd.md` (WRONG — bare `_bmad` must have `{project-root}` prefix)

.agent/skills/bmad-workflow-builder/references/template-substitution-rules.md

@@ -0,0 +1,32 @@
+# Template Substitution Rules
+
+The SKILL-template provides a minimal skeleton: frontmatter, overview, and activation with config loading. Everything beyond that is crafted by the builder based on what was learned during discovery and requirements phases.
+
+## Frontmatter
+
+- `{module-code-or-empty}` → Module code prefix with hyphen (e.g., `bmb-`) or empty for standalone
+- `{skill-name}` → Skill functional name (kebab-case)
+- `{skill-description}` → Two parts: [5-8 word summary]. [trigger phrases]
+
+## Module Conditionals
+
+### For Module-Based Skills
+- `{if-module}` ... `{/if-module}` → Keep the content inside
+- `{if-standalone}` ... `{/if-standalone}` → Remove the entire block including markers
+- `{module-code}` → Module code without trailing hyphen (e.g., `bmb`)
+- `{module-setup-skill}` → Name of the module's setup skill (e.g., `bmad-builder-setup`)
+
+### For Standalone Skills
+- `{if-module}` ... `{/if-module}` → Remove the entire block including markers
+- `{if-standalone}` ... `{/if-standalone}` → Keep the content inside
+
+## Beyond the Template
+
+The builder determines the rest of the skill structure — body sections, phases, stages, scripts, external skills, headless mode, role guidance — based on the skill type classification and requirements gathered during the build process. The template intentionally does not prescribe these; the builder has the context to craft them.
+
+## Path References
+
+All generated skills use `./` prefix for skill-internal paths:
+- `./references/{reference}.md` — Reference documents loaded on demand
+- `./references/{stage}.md` — Stage prompts (complex workflows)
+- `./scripts/` — Python/shell scripts for deterministic operations

.agent/skills/bmad-workflow-builder/report-quality-scan-creator.md

@@ -0,0 +1,247 @@
+# BMad Method · Quality Analysis Report Creator
+
+You synthesize scanner analyses into an actionable quality report. You read all scanner output — structured JSON from lint scripts, free-form analysis from LLM scanners — and produce two outputs: a narrative markdown report for humans and a structured JSON file for the interactive HTML renderer.
+
+Your job is **synthesis, not transcription.** Don't list findings by scanner. Identify themes — root causes that explain clusters of observations across multiple scanners. Lead with what matters most.
+
+## Inputs
+
+- `{skill-path}` — Path to the skill being analyzed
+- `{quality-report-dir}` — Directory containing all scanner output AND where to write your reports
+
+## Process
+
+### Step 1: Read Everything
+
+Read all files in `{quality-report-dir}`:
+- `*-temp.json` — Lint script output (structured JSON with findings arrays)
+- `*-prepass.json` — Pre-pass metrics (structural data, token counts, dependency graphs)
+- `*-analysis.md` — LLM scanner analyses (free-form markdown with assessments, findings, strengths)
+
+### Step 2: Synthesize Themes
+
+This is the most important step. Look across ALL scanner output for **findings that share a root cause** — observations from different scanners that would be resolved by the same fix.
+
+Ask: "If I fixed X, how many findings across all scanners would this resolve?"
+
+Group related findings into 3-5 themes. A theme has:
+- **Name** — clear description of the root cause (e.g., "Over-specification of LLM capabilities")
+- **Description** — what's happening and why it matters (2-3 sentences)
+- **Severity** — highest severity of constituent findings
+- **Impact** — what fixing this would improve (token savings, reliability, adaptability)
+- **Action** — one coherent instruction to address the root cause (not a list of individual fixes)
+- **Constituent findings** — the specific observations from individual scanners that belong to this theme, each with source scanner, file:line, and brief description
+
+Findings that don't fit any theme become standalone items.
+
+### Step 3: Assess Overall Quality
+
+Synthesize a grade and narrative:
+- **Grade:** Excellent (no high+ issues, few medium) / Good (some high or several medium) / Fair (multiple high) / Poor (critical issues)
+- **Narrative:** 2-3 sentences capturing the skill's primary strength and primary opportunity. This is what the user reads first — make it count.
+
+### Step 4: Collect Strengths
+
+Gather strengths from all scanners. Group by theme if natural. These tell the user what NOT to break.
+
+### Step 5: Organize Detailed Analysis
+
+For each analysis dimension (structure, craft, cohesion, efficiency, experience, scripts), summarize the scanner's assessment and list findings not already covered by themes. This is the "deep dive" layer for users who want scanner-level detail.
+
+### Step 6: Rank Recommendations
+
+Order by impact — "how many findings does fixing this resolve?" The fix that clears 9 findings ranks above the fix that clears 1, even at the same severity.
+
+## Write Two Files
+
+### 1. quality-report.md
+
+A narrative markdown report. Structure:
+
+```markdown
+# BMad Method · Quality Analysis: {skill-name}
+
+**Analyzed:** {timestamp} | **Path:** {skill-path}
+**Interactive report:** quality-report.html
+
+## Assessment
+
+**{Grade}** — {narrative}
+
+## What's Broken
+
+{Only if critical/high issues exist. Each with file:line, what's wrong, how to fix.}
+
+## Opportunities
+
+### 1. {Theme Name} ({severity} — {N} observations)
+
+{Description — what's happening, why it matters, what fixing it achieves.}
+
+**Fix:** {One coherent action to address the root cause.}
+
+**Observations:**
+- {finding from scanner X} — file:line
+- {finding from scanner Y} — file:line
+- ...
+
+{Repeat for each theme}
+
+## Strengths
+
+{What the skill does well — preserve these.}
+
+## Detailed Analysis
+
+### Structure & Integrity
+{Assessment + any findings not covered by themes}
+
+### Craft & Writing Quality
+{Assessment + prompt health + any remaining findings}
+
+### Cohesion & Design
+{Assessment + dimension scores + any remaining findings}
+
+### Execution Efficiency
+{Assessment + any remaining findings}
+
+### User Experience
+{Journeys, headless assessment, edge cases}
+
+### Script Opportunities
+{Assessment + token savings estimates}
+
+## Recommendations
+
+1. {Highest impact — resolves N observations}
+2. ...
+3. ...
+```
+
+### 2. report-data.json
+
+**CRITICAL: This file is consumed by a deterministic Python script. Use EXACTLY the field names shown below. Do not rename, restructure, or omit any required fields. The HTML renderer will silently produce empty sections if field names don't match.**
+
+Every `"..."` below is a placeholder for your content. Replace with actual values. Arrays may be empty `[]` but must exist.
+
+```json
+{
+  "meta": {
+    "skill_name": "the-skill-name",
+    "skill_path": "/full/path/to/skill",
+    "timestamp": "2026-03-26T23:03:03Z",
+    "scanner_count": 8
+  },
+  "narrative": "2-3 sentence synthesis shown at top of report",
+  "grade": "Excellent|Good|Fair|Poor",
+  "broken": [
+    {
+      "title": "Short headline of the broken thing",
+      "file": "relative/path.md",
+      "line": 25,
+      "detail": "Why it's broken and what goes wrong",
+      "action": "Specific fix instruction",
+      "severity": "critical|high",
+      "source": "which-scanner"
+    }
+  ],
+  "opportunities": [
+    {
+      "name": "Theme name — MUST use 'name' not 'title'",
+      "description": "What's happening and why it matters",
+      "severity": "high|medium|low",
+      "impact": "What fixing this achieves",
+      "action": "One coherent fix instruction for the whole theme",
+      "finding_count": 9,
+      "findings": [
+        {
+          "title": "Individual observation headline",
+          "file": "relative/path.md",
+          "line": 42,
+          "detail": "What was observed",
+          "source": "which-scanner"
+        }
+      ]
+    }
+  ],
+  "strengths": [
+    {
+      "title": "What's strong — MUST be an object with 'title', not a plain string",
+      "detail": "Why it matters and should be preserved"
+    }
+  ],
+  "detailed_analysis": {
+    "structure": {
+      "assessment": "1-3 sentence summary from structure/integrity scanner",
+      "findings": []
+    },
+    "craft": {
+      "assessment": "1-3 sentence summary from prompt-craft scanner",
+      "overview_quality": "appropriate|excessive|missing",
+      "progressive_disclosure": "good|needs-extraction|monolithic",
+      "findings": []
+    },
+    "cohesion": {
+      "assessment": "1-3 sentence summary from cohesion scanner",
+      "dimensions": {
+        "stage_flow": { "score": "strong|moderate|weak", "notes": "explanation" }
+      },
+      "findings": []
+    },
+    "efficiency": {
+      "assessment": "1-3 sentence summary from efficiency scanner",
+      "findings": []
+    },
+    "experience": {
+      "assessment": "1-3 sentence summary from enhancement scanner",
+      "journeys": [
+        {
+          "archetype": "first-timer|expert|confused|edge-case|hostile-environment|automator",
+          "summary": "Brief narrative of this user's experience",
+          "friction_points": ["moment where user struggles"],
+          "bright_spots": ["moment where skill shines"]
+        }
+      ],
+      "autonomous": {
+        "potential": "headless-ready|easily-adaptable|partially-adaptable|fundamentally-interactive",
+        "notes": "Brief assessment"
+      },
+      "findings": []
+    },
+    "scripts": {
+      "assessment": "1-3 sentence summary from script-opportunities scanner",
+      "token_savings": "estimated total",
+      "findings": []
+    }
+  },
+  "recommendations": [
+    {
+      "rank": 1,
+      "action": "What to do — MUST use 'action' not 'description'",
+      "resolves": 9,
+      "effort": "low|medium|high"
+    }
+  ]
+}
+```
+
+**Self-check before writing report-data.json:**
+1. Is `meta.skill_name` present (not `meta.skill` or `meta.name`)?
+2. Is `meta.scanner_count` a number (not an array of scanner names)?
+3. Is every strength an object `{"title": "...", "detail": "..."}` (not a plain string)?
+4. Does every opportunity use `name` (not `title`) and include `finding_count` and `findings` array?
+5. Does every recommendation use `action` (not `description`) and include `rank` number?
+6. Are `broken`, `opportunities`, `strengths`, `recommendations` all arrays (even if empty)?
+7. Are detailed_analysis keys exactly: `structure`, `craft`, `cohesion`, `efficiency`, `experience`, `scripts`?
+8. Does every journey use `archetype` (not `persona`), `summary` (not `friction`), `friction_points` array, `bright_spots` array?
+9. Does `autonomous` use `potential` and `notes`?
+
+Write both files to `{quality-report-dir}/`.
+
+## Return
+
+Return only the path to `report-data.json` when complete.
+
+## Key Principle
+
+You are the synthesis layer. Scanners analyze through individual lenses. You connect the dots. A user reading your report should understand the 3 most important things about their skill within 30 seconds — not wade through 14 individual findings organized by which scanner found them.

.agent/skills/bmad-workflow-builder/scripts/generate-html-report.py

@@ -0,0 +1,539 @@
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+#!/usr/bin/env python3
+"""
+Generate an interactive HTML quality analysis report from report-data.json.
+
+Reads the structured report data produced by the report creator and renders
+a self-contained HTML report with:
+  - Grade + narrative at top
+  - Broken items with fix prompts
+  - Opportunity themes with "Fix This Theme" prompt generation
+  - Expandable strengths
+  - Expandable detailed analysis per dimension
+  - Link to full markdown report
+
+Usage:
+  python3 generate-html-report.py {quality-report-dir} [--open]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import platform
+import subprocess
+import sys
+from pathlib import Path
+
+
+def load_report_data(report_dir: Path) -> dict:
+    """Load report-data.json from the report directory."""
+    data_file = report_dir / 'report-data.json'
+    if not data_file.exists():
+        print(f'Error: {data_file} not found', file=sys.stderr)
+        sys.exit(2)
+    return json.loads(data_file.read_text(encoding='utf-8'))
+
+
+def build_fix_prompt(skill_path: str, theme: dict) -> str:
+    """Build a coherent fix prompt for an entire opportunity theme."""
+    prompt = f"## Task: {theme['name']}\n"
+    prompt += f"Skill path: {skill_path}\n\n"
+    prompt += f"### Problem\n{theme['description']}\n\n"
+    prompt += f"### Fix\n{theme['action']}\n\n"
+    if theme.get('findings'):
+        prompt += "### Specific observations to address:\n\n"
+        for i, f in enumerate(theme['findings'], 1):
+            loc = f"{f['file']}:{f['line']}" if f.get('file') and f.get('line') else f.get('file', '')
+            prompt += f"{i}. **{f['title']}**"
+            if loc:
+                prompt += f" ({loc})"
+            if f.get('detail'):
+                prompt += f"\n   {f['detail']}"
+            prompt += "\n"
+    return prompt.strip()
+
+
+def build_broken_prompt(skill_path: str, items: list) -> str:
+    """Build a fix prompt for all broken items."""
+    prompt = f"## Task: Fix Critical Issues\nSkill path: {skill_path}\n\n"
+    for i, item in enumerate(items, 1):
+        loc = f"{item['file']}:{item['line']}" if item.get('file') and item.get('line') else item.get('file', '')
+        prompt += f"{i}. **[{item.get('severity','high').upper()}] {item['title']}**\n"
+        if loc:
+            prompt += f"   File: {loc}\n"
+        if item.get('detail'):
+            prompt += f"   Context: {item['detail']}\n"
+        if item.get('action'):
+            prompt += f"   Fix: {item['action']}\n"
+        prompt += "\n"
+    return prompt.strip()
+
+
+HTML_TEMPLATE = r"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>BMad Method · Quality Analysis: SKILL_NAME</title>
+<style>
+:root {
+  --bg: #0d1117; --surface: #161b22; --surface2: #21262d; --border: #30363d;
+  --text: #e6edf3; --text-muted: #8b949e; --text-dim: #6e7681;
+  --critical: #f85149; --high: #f0883e; --medium: #d29922; --low: #58a6ff;
+  --strength: #3fb950; --suggestion: #a371f7;
+  --accent: #58a6ff; --accent-hover: #79c0ff;
+  --font: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif;
+  --mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, monospace;
+}
+@media (prefers-color-scheme: light) {
+  :root {
+    --bg: #ffffff; --surface: #f6f8fa; --surface2: #eaeef2; --border: #d0d7de;
+    --text: #1f2328; --text-muted: #656d76; --text-dim: #8c959f;
+    --critical: #cf222e; --high: #bc4c00; --medium: #9a6700; --low: #0969da;
+    --strength: #1a7f37; --suggestion: #8250df;
+    --accent: #0969da; --accent-hover: #0550ae;
+  }
+}
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { font-family: var(--font); background: var(--bg); color: var(--text); line-height: 1.5; padding: 2rem; max-width: 900px; margin: 0 auto; }
+h1 { font-size: 1.5rem; margin-bottom: 0.25rem; }
+.subtitle { color: var(--text-muted); font-size: 0.85rem; margin-bottom: 1.5rem; }
+.subtitle a { color: var(--accent); text-decoration: none; }
+.subtitle a:hover { text-decoration: underline; }
+.grade { font-size: 2.5rem; font-weight: 700; margin: 0.5rem 0; }
+.grade-Excellent { color: var(--strength); }
+.grade-Good { color: var(--low); }
+.grade-Fair { color: var(--medium); }
+.grade-Poor { color: var(--critical); }
+.narrative { color: var(--text-muted); font-size: 0.95rem; margin-bottom: 1.5rem; line-height: 1.6; }
+.badge { display: inline-flex; align-items: center; padding: 0.15rem 0.5rem; border-radius: 2rem; font-size: 0.75rem; font-weight: 600; }
+.badge-critical { background: color-mix(in srgb, var(--critical) 20%, transparent); color: var(--critical); }
+.badge-high { background: color-mix(in srgb, var(--high) 20%, transparent); color: var(--high); }
+.badge-medium { background: color-mix(in srgb, var(--medium) 20%, transparent); color: var(--medium); }
+.badge-low { background: color-mix(in srgb, var(--low) 20%, transparent); color: var(--low); }
+.badge-strength { background: color-mix(in srgb, var(--strength) 20%, transparent); color: var(--strength); }
+.section { border: 1px solid var(--border); border-radius: 0.5rem; margin: 0.75rem 0; overflow: hidden; }
+.section-header { display: flex; align-items: center; gap: 0.75rem; padding: 0.75rem 1rem; background: var(--surface); cursor: pointer; user-select: none; }
+.section-header:hover { background: var(--surface2); }
+.section-header .arrow { font-size: 0.7rem; transition: transform 0.15s; color: var(--text-muted); width: 1rem; }
+.section-header.open .arrow { transform: rotate(90deg); }
+.section-header .label { font-weight: 600; flex: 1; }
+.section-header .count { font-size: 0.8rem; color: var(--text-muted); }
+.section-header .actions { display: flex; gap: 0.5rem; }
+.section-body { display: none; }
+.section-body.open { display: block; }
+.item { padding: 0.75rem 1rem; border-top: 1px solid var(--border); }
+.item:hover { background: var(--surface); }
+.item-title { font-weight: 600; font-size: 0.9rem; }
+.item-file { font-family: var(--mono); font-size: 0.75rem; color: var(--text-muted); }
+.item-desc { font-size: 0.85rem; color: var(--text-muted); margin-top: 0.25rem; }
+.item-action { font-size: 0.85rem; margin-top: 0.25rem; }
+.item-action strong { color: var(--strength); }
+.opp { padding: 1rem; border-top: 1px solid var(--border); }
+.opp-header { display: flex; align-items: center; gap: 0.75rem; }
+.opp-name { font-weight: 600; font-size: 1rem; flex: 1; }
+.opp-count { font-size: 0.8rem; color: var(--text-muted); }
+.opp-desc { font-size: 0.9rem; color: var(--text-muted); margin: 0.5rem 0; }
+.opp-impact { font-size: 0.85rem; color: var(--text-dim); font-style: italic; }
+.opp-findings { margin-top: 0.75rem; padding-left: 1rem; border-left: 2px solid var(--border); display: none; }
+.opp-findings.open { display: block; }
+.opp-finding { font-size: 0.85rem; padding: 0.25rem 0; color: var(--text-muted); }
+.opp-finding .source { font-size: 0.75rem; color: var(--text-dim); }
+.btn { background: none; border: 1px solid var(--border); border-radius: 0.25rem; padding: 0.3rem 0.7rem; cursor: pointer; color: var(--text-muted); font-size: 0.8rem; transition: all 0.15s; }
+.btn:hover { border-color: var(--accent); color: var(--accent); }
+.btn-primary { background: var(--accent); color: #fff; border-color: var(--accent); font-weight: 600; }
+.btn-primary:hover { background: var(--accent-hover); }
+.btn.copied { border-color: var(--strength); color: var(--strength); }
+.strength-item { padding: 0.5rem 1rem; border-top: 1px solid var(--border); }
+.strength-item .title { font-weight: 600; font-size: 0.9rem; color: var(--strength); }
+.strength-item .detail { font-size: 0.85rem; color: var(--text-muted); }
+.analysis-section { padding: 0.75rem 1rem; border-top: 1px solid var(--border); }
+.analysis-section h4 { font-size: 0.9rem; margin-bottom: 0.25rem; }
+.analysis-section p { font-size: 0.85rem; color: var(--text-muted); }
+.analysis-finding { font-size: 0.85rem; padding: 0.25rem 0 0.25rem 1rem; border-left: 2px solid var(--border); margin: 0.25rem 0; color: var(--text-muted); }
+.modal-overlay { display: none; position: fixed; inset: 0; background: rgba(0,0,0,0.6); z-index: 200; align-items: center; justify-content: center; }
+.modal-overlay.visible { display: flex; }
+.modal { background: var(--surface); border: 1px solid var(--border); border-radius: 0.5rem; padding: 1.5rem; width: 90%; max-width: 700px; max-height: 80vh; overflow-y: auto; }
+.modal h3 { margin-bottom: 0.75rem; }
+.modal pre { background: var(--bg); border: 1px solid var(--border); border-radius: 0.375rem; padding: 1rem; font-family: var(--mono); font-size: 0.8rem; white-space: pre-wrap; word-wrap: break-word; max-height: 50vh; overflow-y: auto; }
+.modal-actions { display: flex; gap: 0.75rem; margin-top: 1rem; justify-content: flex-end; }
+.recs { padding: 0.75rem 1rem; border-top: 1px solid var(--border); }
+.rec { padding: 0.3rem 0; font-size: 0.9rem; }
+.rec-rank { font-weight: 700; color: var(--accent); margin-right: 0.5rem; }
+.rec-resolves { font-size: 0.8rem; color: var(--text-dim); }
+</style>
+</head>
+<body>
+
+<div style="color:#a371f7;font-size:0.8rem;font-weight:600;letter-spacing:0.05em;text-transform:uppercase;margin-bottom:0.25rem">BMad Method</div>
+<h1>Quality Analysis: <span id="skill-name"></span></h1>
+<div class="subtitle" id="subtitle"></div>
+
+<div id="grade-area"></div>
+<div class="narrative" id="narrative"></div>
+
+<div id="broken-section"></div>
+<div id="opportunities-section"></div>
+<div id="strengths-section"></div>
+<div id="recommendations-section"></div>
+<div id="detailed-section"></div>
+
+<div class="modal-overlay" id="modal" onclick="if(event.target===this)closeModal()">
+  <div class="modal">
+    <h3 id="modal-title">Generated Prompt</h3>
+    <pre id="modal-content"></pre>
+    <div class="modal-actions">
+      <button class="btn" onclick="closeModal()">Close</button>
+      <button class="btn btn-primary" onclick="copyModal()">Copy to Clipboard</button>
+    </div>
+  </div>
+</div>
+
+<script>
+const RAW = JSON.parse(document.getElementById('report-data').textContent);
+const DATA = normalize(RAW);
+
+function normalize(d) {
+  // Fix meta field variants
+  if (d.meta) {
+    d.meta.skill_name = d.meta.skill_name || d.meta.skill || d.meta.name || 'Unknown';
+    d.meta.scanner_count = typeof d.meta.scanner_count === 'number' ? d.meta.scanner_count
+      : Array.isArray(d.meta.scanners_run) ? d.meta.scanners_run.length
+      : d.meta.scanner_count || 0;
+  }
+  // Fix strengths: plain strings → objects
+  d.strengths = (d.strengths || []).map(s =>
+    typeof s === 'string' ? { title: s, detail: '' } : { title: s.title || '', detail: s.detail || '' }
+  );
+  // Fix opportunities: title→name, findings_resolved→findings
+  (d.opportunities || []).forEach(o => {
+    o.name = o.name || o.title || '';
+    o.finding_count = o.finding_count || (o.findings || o.findings_resolved || []).length;
+    if (!o.findings && o.findings_resolved) o.findings = [];
+    o.action = o.action || o.fix || '';
+  });
+  // Fix broken: description→detail, fix→action
+  (d.broken || []).forEach(b => {
+    b.detail = b.detail || b.description || '';
+    b.action = b.action || b.fix || '';
+  });
+  // Fix recommendations: description→action
+  (d.recommendations || []).forEach((r, i) => {
+    r.action = r.action || r.description || '';
+    r.rank = r.rank || i + 1;
+  });
+  // Fix journeys: persona→archetype, friction→friction_points
+  if (d.detailed_analysis && d.detailed_analysis.experience) {
+    d.detailed_analysis.experience.journeys = (d.detailed_analysis.experience.journeys || []).map(j => ({
+      archetype: j.archetype || j.persona || j.name || 'Unknown',
+      summary: j.summary || j.journey_summary || j.description || j.friction || '',
+      friction_points: j.friction_points || (j.friction ? [j.friction] : []),
+      bright_spots: j.bright_spots || (j.bright ? [j.bright] : [])
+    }));
+  }
+  return d;
+}
+
+function esc(s) {
+  if (!s) return '';
+  const d = document.createElement('div');
+  d.textContent = String(s);
+  return d.innerHTML;
+}
+
+function init() {
+  const m = DATA.meta;
+  document.getElementById('skill-name').textContent = m.skill_name;
+  document.getElementById('subtitle').innerHTML =
+    `${esc(m.skill_path)} &bull; ${m.timestamp ? m.timestamp.split('T')[0] : ''} &bull; ${m.scanner_count || 0} scanners &bull; <a href="quality-report.md">Full Report &nearr;</a>`;
+
+  document.getElementById('grade-area').innerHTML =
+    `<div class="grade grade-${DATA.grade}">${esc(DATA.grade)}</div>`;
+  document.getElementById('narrative').textContent = DATA.narrative || '';
+
+  renderBroken();
+  renderOpportunities();
+  renderStrengths();
+  renderRecommendations();
+  renderDetailed();
+}
+
+function renderBroken() {
+  const items = DATA.broken || [];
+  if (!items.length) return;
+  let html = `<div class="section"><div class="section-header open" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Broken / Critical (${items.length})</span>`;
+  html += `<div class="actions"><button class="btn btn-primary" onclick="event.stopPropagation();showBrokenPrompt()">Fix These</button></div>`;
+  html += `</div><div class="section-body open">`;
+  items.forEach(item => {
+    const loc = item.file ? `${item.file}${item.line ? ':'+item.line : ''}` : '';
+    html += `<div class="item">`;
+    html += `<span class="badge badge-${item.severity || 'high'}">${esc(item.severity || 'high')}</span> `;
+    if (loc) html += `<span class="item-file">${esc(loc)}</span>`;
+    html += `<div class="item-title">${esc(item.title)}</div>`;
+    if (item.detail) html += `<div class="item-desc">${esc(item.detail)}</div>`;
+    if (item.action) html += `<div class="item-action"><strong>Fix:</strong> ${esc(item.action)}</div>`;
+    html += `</div>`;
+  });
+  html += `</div></div>`;
+  document.getElementById('broken-section').innerHTML = html;
+}
+
+function renderOpportunities() {
+  const opps = DATA.opportunities || [];
+  if (!opps.length) return;
+  let html = `<div class="section"><div class="section-header open" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Opportunities (${opps.length})</span>`;
+  html += `</div><div class="section-body open">`;
+  opps.forEach((opp, idx) => {
+    html += `<div class="opp">`;
+    html += `<div class="opp-header">`;
+    html += `<span class="badge badge-${opp.severity || 'medium'}">${esc(opp.severity || 'medium')}</span>`;
+    html += `<span class="opp-name">${idx+1}. ${esc(opp.name)}</span>`;
+    html += `<span class="opp-count">${opp.finding_count || (opp.findings||[]).length} observations</span>`;
+    html += `<button class="btn" onclick="toggleFindings(${idx})">Details</button>`;
+    html += `<button class="btn btn-primary" onclick="showThemePrompt(${idx})">Fix This</button>`;
+    html += `</div>`;
+    html += `<div class="opp-desc">${esc(opp.description)}</div>`;
+    if (opp.impact) html += `<div class="opp-impact">Impact: ${esc(opp.impact)}</div>`;
+    html += `<div class="opp-findings" id="findings-${idx}">`;
+    (opp.findings || []).forEach(f => {
+      const loc = f.file ? `${f.file}${f.line ? ':'+f.line : ''}` : '';
+      html += `<div class="opp-finding">`;
+      html += `<strong>${esc(f.title)}</strong>`;
+      if (loc) html += ` <span class="item-file">${esc(loc)}</span>`;
+      if (f.source) html += ` <span class="source">[${esc(f.source)}]</span>`;
+      if (f.detail) html += `<br>${esc(f.detail)}`;
+      html += `</div>`;
+    });
+    html += `</div></div>`;
+  });
+  html += `</div></div>`;
+  document.getElementById('opportunities-section').innerHTML = html;
+}
+
+function renderStrengths() {
+  const items = DATA.strengths || [];
+  if (!items.length) return;
+  let html = `<div class="section"><div class="section-header" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Strengths (${items.length})</span>`;
+  html += `</div><div class="section-body">`;
+  items.forEach(s => {
+    html += `<div class="strength-item"><div class="title">${esc(s.title)}</div>`;
+    if (s.detail) html += `<div class="detail">${esc(s.detail)}</div>`;
+    html += `</div>`;
+  });
+  html += `</div></div>`;
+  document.getElementById('strengths-section').innerHTML = html;
+}
+
+function renderRecommendations() {
+  const recs = DATA.recommendations || [];
+  if (!recs.length) return;
+  let html = `<div class="section"><div class="section-header open" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Recommendations</span>`;
+  html += `</div><div class="section-body open"><div class="recs">`;
+  recs.forEach(r => {
+    html += `<div class="rec">`;
+    html += `<span class="rec-rank">#${r.rank}</span>`;
+    html += `${esc(r.action)}`;
+    if (r.resolves) html += ` <span class="rec-resolves">(resolves ${r.resolves} observations)</span>`;
+    html += `</div>`;
+  });
+  html += `</div></div></div>`;
+  document.getElementById('recommendations-section').innerHTML = html;
+}
+
+function renderDetailed() {
+  const da = DATA.detailed_analysis;
+  if (!da) return;
+  const dims = [
+    ['structure', 'Structure & Integrity'],
+    ['craft', 'Craft & Writing Quality'],
+    ['cohesion', 'Cohesion & Design'],
+    ['efficiency', 'Execution Efficiency'],
+    ['experience', 'User Experience'],
+    ['scripts', 'Script Opportunities']
+  ];
+  let html = `<div class="section"><div class="section-header" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Detailed Analysis</span>`;
+  html += `</div><div class="section-body">`;
+  dims.forEach(([key, label]) => {
+    const dim = da[key];
+    if (!dim) return;
+    html += `<div class="analysis-section"><h4>${label}</h4>`;
+    if (dim.assessment) html += `<p>${esc(dim.assessment)}</p>`;
+    if (dim.dimensions) {
+      html += `<table style="width:100%;font-size:0.85rem;margin:0.5rem 0;border-collapse:collapse;">`;
+      html += `<tr><th style="text-align:left;padding:0.3rem;border-bottom:1px solid var(--border)">Dimension</th><th style="text-align:left;padding:0.3rem;border-bottom:1px solid var(--border)">Score</th><th style="text-align:left;padding:0.3rem;border-bottom:1px solid var(--border)">Notes</th></tr>`;
+      Object.entries(dim.dimensions).forEach(([d, v]) => {
+        if (v && typeof v === 'object') {
+          html += `<tr><td style="padding:0.3rem;border-bottom:1px solid var(--border)">${esc(d.replace(/_/g,' '))}</td><td style="padding:0.3rem;border-bottom:1px solid var(--border)">${esc(v.score||'')}</td><td style="padding:0.3rem;border-bottom:1px solid var(--border)">${esc(v.notes||'')}</td></tr>`;
+        }
+      });
+      html += `</table>`;
+    }
+    if (dim.journeys && dim.journeys.length) {
+      dim.journeys.forEach(j => {
+        html += `<div style="margin:0.5rem 0"><strong>${esc(j.archetype)}</strong>: ${esc(j.summary || j.journey_summary || '')}`;
+        if (j.friction_points && j.friction_points.length) {
+          html += `<ul style="color:var(--high);font-size:0.85rem;padding-left:1.25rem">`;
+          j.friction_points.forEach(fp => { html += `<li>${esc(fp)}</li>`; });
+          html += `</ul>`;
+        }
+        html += `</div>`;
+      });
+    }
+    if (dim.autonomous) {
+      const a = dim.autonomous;
+      html += `<p><strong>Headless Potential:</strong> ${esc(a.potential||'')}`;
+      if (a.notes) html += ` — ${esc(a.notes)}`;
+      html += `</p>`;
+    }
+    (dim.findings || []).forEach(f => {
+      const loc = f.file ? `${f.file}${f.line ? ':'+f.line : ''}` : '';
+      html += `<div class="analysis-finding">`;
+      if (f.severity) html += `<span class="badge badge-${f.severity}">${esc(f.severity)}</span> `;
+      html += `${esc(f.title)}`;
+      if (loc) html += ` <span class="item-file">${esc(loc)}</span>`;
+      html += `</div>`;
+    });
+    html += `</div>`;
+  });
+  html += `</div></div>`;
+  document.getElementById('detailed-section').innerHTML = html;
+}
+
+// --- Interactions ---
+function toggleSection(el) {
+  el.classList.toggle('open');
+  el.nextElementSibling.classList.toggle('open');
+}
+
+function toggleFindings(idx) {
+  document.getElementById('findings-'+idx).classList.toggle('open');
+}
+
+// --- Prompt Generation ---
+function showThemePrompt(idx) {
+  const opp = DATA.opportunities[idx];
+  if (!opp) return;
+  let prompt = `## Task: ${opp.name}\nSkill path: ${DATA.meta.skill_path}\n\n`;
+  prompt += `### Problem\n${opp.description}\n\n`;
+  prompt += `### Fix\n${opp.action}\n\n`;
+  if (opp.findings && opp.findings.length) {
+    prompt += `### Specific observations to address:\n\n`;
+    opp.findings.forEach((f, i) => {
+      const loc = f.file ? (f.line ? `${f.file}:${f.line}` : f.file) : '';
+      prompt += `${i+1}. **${f.title}**`;
+      if (loc) prompt += ` (${loc})`;
+      if (f.detail) prompt += `\n   ${f.detail}`;
+      prompt += `\n`;
+    });
+  }
+  document.getElementById('modal-title').textContent = `Fix: ${opp.name}`;
+  document.getElementById('modal-content').textContent = prompt.trim();
+  document.getElementById('modal').classList.add('visible');
+}
+
+function showBrokenPrompt() {
+  const items = DATA.broken || [];
+  let prompt = `## Task: Fix Critical Issues\nSkill path: ${DATA.meta.skill_path}\n\n`;
+  items.forEach((item, i) => {
+    const loc = item.file ? (item.line ? `${item.file}:${item.line}` : item.file) : '';
+    prompt += `${i+1}. **[${(item.severity||'high').toUpperCase()}] ${item.title}**\n`;
+    if (loc) prompt += `   File: ${loc}\n`;
+    if (item.detail) prompt += `   Context: ${item.detail}\n`;
+    if (item.action) prompt += `   Fix: ${item.action}\n`;
+    prompt += `\n`;
+  });
+  document.getElementById('modal-title').textContent = 'Fix Critical Issues';
+  document.getElementById('modal-content').textContent = prompt.trim();
+  document.getElementById('modal').classList.add('visible');
+}
+
+function closeModal() { document.getElementById('modal').classList.remove('visible'); }
+
+function copyModal() {
+  const text = document.getElementById('modal-content').textContent;
+  navigator.clipboard.writeText(text).then(() => {
+    const btn = document.querySelector('.modal .btn-primary');
+    btn.textContent = 'Copied!';
+    setTimeout(() => { btn.textContent = 'Copy to Clipboard'; }, 1500);
+  });
+}
+
+init();
+</script>
+</body>
+</html>"""
+
+
+def generate_html(report_data: dict) -> str:
+    """Inject report data into the HTML template."""
+    data_json = json.dumps(report_data, indent=None, ensure_ascii=False)
+    data_tag = f'<script id="report-data" type="application/json">{data_json}</script>'
+    html = HTML_TEMPLATE.replace('<script>\nconst RAW', f'{data_tag}\n<script>\nconst RAW')
+    html = html.replace('SKILL_NAME', report_data.get('meta', {}).get('skill_name', 'Unknown'))
+    return html
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Generate interactive HTML quality analysis report',
+    )
+    parser.add_argument(
+        'report_dir',
+        type=Path,
+        help='Directory containing report-data.json',
+    )
+    parser.add_argument(
+        '--open',
+        action='store_true',
+        help='Open the HTML report in the default browser',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Output HTML file path (default: {report_dir}/quality-report.html)',
+    )
+    args = parser.parse_args()
+
+    if not args.report_dir.is_dir():
+        print(f'Error: {args.report_dir} is not a directory', file=sys.stderr)
+        return 2
+
+    report_data = load_report_data(args.report_dir)
+    html = generate_html(report_data)
+
+    output_path = args.output or (args.report_dir / 'quality-report.html')
+    output_path.write_text(html, encoding='utf-8')
+
+    # Output summary
+    opp_count = len(report_data.get('opportunities', []))
+    broken_count = len(report_data.get('broken', []))
+    print(json.dumps({
+        'html_report': str(output_path),
+        'grade': report_data.get('grade', 'Unknown'),
+        'opportunities': opp_count,
+        'broken': broken_count,
+    }))
+
+    if args.open:
+        system = platform.system()
+        if system == 'Darwin':
+            subprocess.run(['open', str(output_path)])
+        elif system == 'Linux':
+            subprocess.run(['xdg-open', str(output_path)])
+        elif system == 'Windows':
+            subprocess.run(['start', str(output_path)], shell=True)
+
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

.agent/skills/bmad-workflow-builder/scripts/prepass-execution-deps.py

@@ -0,0 +1,288 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for execution efficiency scanner.
+
+Extracts dependency graph data and execution patterns from a BMad skill
+so the LLM scanner can evaluate efficiency from compact structured data.
+
+Covers:
+- Dependency graph from skill structure
+
+- Circular dependency detection
+- Transitive dependency redundancy
+- Parallelizable stage groups (independent nodes)
+- Sequential pattern detection in prompts (numbered Read/Grep/Glob steps)
+- Subagent-from-subagent detection
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+def detect_cycles(graph: dict[str, list[str]]) -> list[list[str]]:
+    """Detect circular dependencies in a directed graph using DFS."""
+    cycles = []
+    visited = set()
+    path = []
+    path_set = set()
+
+    def dfs(node: str) -> None:
+        if node in path_set:
+            cycle_start = path.index(node)
+            cycles.append(path[cycle_start:] + [node])
+            return
+        if node in visited:
+            return
+        visited.add(node)
+        path.append(node)
+        path_set.add(node)
+        for neighbor in graph.get(node, []):
+            dfs(neighbor)
+        path.pop()
+        path_set.discard(node)
+
+    for node in graph:
+        dfs(node)
+
+    return cycles
+
+
+def find_transitive_redundancy(graph: dict[str, list[str]]) -> list[dict]:
+    """Find cases where A declares dependency on C, but A->B->C already exists."""
+    redundancies = []
+
+    def get_transitive(node: str, visited: set | None = None) -> set[str]:
+        if visited is None:
+            visited = set()
+        for dep in graph.get(node, []):
+            if dep not in visited:
+                visited.add(dep)
+                get_transitive(dep, visited)
+        return visited
+
+    for node, direct_deps in graph.items():
+        for dep in direct_deps:
+            # Check if dep is reachable through other direct deps
+            other_deps = [d for d in direct_deps if d != dep]
+            for other in other_deps:
+                transitive = get_transitive(other)
+                if dep in transitive:
+                    redundancies.append({
+                        'node': node,
+                        'redundant_dep': dep,
+                        'already_via': other,
+                        'issue': f'"{node}" declares "{dep}" as dependency, but already reachable via "{other}"',
+                    })
+
+    return redundancies
+
+
+def find_parallel_groups(graph: dict[str, list[str]], all_nodes: set[str]) -> list[list[str]]:
+    """Find groups of nodes that have no dependencies on each other (can run in parallel)."""
+    # Nodes with no incoming edges from other nodes in the set
+    independent_groups = []
+
+    # Simple approach: find all nodes at each "level" of the DAG
+    remaining = set(all_nodes)
+    while remaining:
+        # Nodes whose dependencies are all satisfied (not in remaining)
+        ready = set()
+        for node in remaining:
+            deps = set(graph.get(node, []))
+            if not deps & remaining:
+                ready.add(node)
+        if not ready:
+            break  # Circular dependency, can't proceed
+        if len(ready) > 1:
+            independent_groups.append(sorted(ready))
+        remaining -= ready
+
+    return independent_groups
+
+
+def scan_sequential_patterns(filepath: Path, rel_path: str) -> list[dict]:
+    """Detect sequential operation patterns that could be parallel."""
+    content = filepath.read_text(encoding='utf-8')
+    patterns = []
+
+    # Sequential numbered steps with Read/Grep/Glob
+    tool_steps = re.findall(
+        r'^\s*\d+\.\s+.*?\b(Read|Grep|Glob|read|grep|glob)\b.*$',
+        content, re.MULTILINE
+    )
+    if len(tool_steps) >= 3:
+        patterns.append({
+            'file': rel_path,
+            'type': 'sequential-tool-calls',
+            'count': len(tool_steps),
+            'issue': f'{len(tool_steps)} sequential tool call steps found — check if independent calls can be parallel',
+        })
+
+    # "Read all files" / "for each" loop patterns
+    loop_patterns = [
+        (r'[Rr]ead all (?:files|documents|prompts)', 'read-all'),
+        (r'[Ff]or each (?:file|document|prompt|stage)', 'for-each-loop'),
+        (r'[Aa]nalyze each', 'analyze-each'),
+        (r'[Ss]can (?:through|all|each)', 'scan-all'),
+        (r'[Rr]eview (?:all|each)', 'review-all'),
+    ]
+    for pattern, ptype in loop_patterns:
+        matches = re.findall(pattern, content)
+        if matches:
+            patterns.append({
+                'file': rel_path,
+                'type': ptype,
+                'count': len(matches),
+                'issue': f'"{matches[0]}" pattern found — consider parallel subagent delegation',
+            })
+
+    # Subagent spawning from subagent (impossible)
+    if re.search(r'(?i)spawn.*subagent|launch.*subagent|create.*subagent', content):
+        # Check if this file IS a subagent (non-SKILL.md, non-numbered prompt at root)
+        if rel_path != 'SKILL.md' and not re.match(r'^\d+-', rel_path):
+            patterns.append({
+                'file': rel_path,
+                'type': 'subagent-chain-violation',
+                'count': 1,
+                'issue': 'Subagent file references spawning other subagents — subagents cannot spawn subagents',
+            })
+
+    return patterns
+
+
+def scan_execution_deps(skill_path: Path) -> dict:
+    """Run all deterministic execution efficiency checks."""
+    # Build dependency graph from skill structure
+    dep_graph: dict[str, list[str]] = {}
+    prefer_after: dict[str, list[str]] = {}
+    all_stages: set[str] = set()
+
+    # Check for stage-level prompt files at skill root
+    for f in sorted(skill_path.iterdir()):
+        if f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md':
+            all_stages.add(f.stem)
+
+    # Cycle detection
+    cycles = detect_cycles(dep_graph)
+
+    # Transitive redundancy
+    redundancies = find_transitive_redundancy(dep_graph)
+
+    # Parallel groups
+    parallel_groups = find_parallel_groups(dep_graph, all_stages)
+
+    # Sequential pattern detection across all prompt and agent files at root
+    sequential_patterns = []
+    for f in sorted(skill_path.iterdir()):
+        if f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md':
+            patterns = scan_sequential_patterns(f, f.name)
+            sequential_patterns.extend(patterns)
+
+    # Also scan SKILL.md
+    skill_md = skill_path / 'SKILL.md'
+    if skill_md.exists():
+        sequential_patterns.extend(scan_sequential_patterns(skill_md, 'SKILL.md'))
+
+    # Build issues from deterministic findings
+    issues = []
+    for cycle in cycles:
+        issues.append({
+            'severity': 'critical',
+            'category': 'circular-dependency',
+            'issue': f'Circular dependency detected: {" → ".join(cycle)}',
+        })
+    for r in redundancies:
+        issues.append({
+            'severity': 'medium',
+            'category': 'dependency-bloat',
+            'issue': r['issue'],
+        })
+    for p in sequential_patterns:
+        severity = 'critical' if p['type'] == 'subagent-chain-violation' else 'medium'
+        issues.append({
+            'file': p['file'],
+            'severity': severity,
+            'category': p['type'],
+            'issue': p['issue'],
+        })
+
+    by_severity = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0}
+    for issue in issues:
+        sev = issue['severity']
+        if sev in by_severity:
+            by_severity[sev] += 1
+
+    status = 'pass'
+    if by_severity['critical'] > 0:
+        status = 'fail'
+    elif by_severity['medium'] > 0:
+        status = 'warning'
+
+    return {
+        'scanner': 'execution-efficiency-prepass',
+        'script': 'prepass-execution-deps.py',
+        'version': '1.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': status,
+        'dependency_graph': {
+            'stages': sorted(all_stages),
+            'hard_dependencies': dep_graph,
+            'soft_dependencies': prefer_after,
+            'cycles': cycles,
+            'transitive_redundancies': redundancies,
+            'parallel_groups': parallel_groups,
+        },
+        'sequential_patterns': sequential_patterns,
+        'issues': issues,
+        'summary': {
+            'total_issues': len(issues),
+            'by_severity': by_severity,
+        },
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Extract execution dependency graph and patterns for LLM scanner pre-pass',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_execution_deps(args.skill_path)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

.agent/skills/bmad-workflow-builder/scripts/prepass-prompt-metrics.py

@@ -0,0 +1,285 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for prompt craft scanner.
+
+Extracts metrics and flagged patterns from SKILL.md and prompt files
+so the LLM scanner can work from compact data instead of reading raw files.
+
+Covers:
+- SKILL.md line count and section inventory
+- Overview section size
+- Inline data detection (tables, fenced code blocks)
+- Defensive padding pattern grep
+- Meta-explanation pattern grep
+- Back-reference detection ("as described above")
+- Config header and progression condition presence per prompt
+- File-level token estimates (chars / 4 rough approximation)
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+# Defensive padding / filler patterns
+WASTE_PATTERNS = [
+    (r'\b[Mm]ake sure (?:to|you)\b', 'defensive-padding', 'Defensive: "make sure to/you"'),
+    (r"\b[Dd]on'?t forget (?:to|that)\b", 'defensive-padding', "Defensive: \"don't forget\""),
+    (r'\b[Rr]emember (?:to|that)\b', 'defensive-padding', 'Defensive: "remember to/that"'),
+    (r'\b[Bb]e sure to\b', 'defensive-padding', 'Defensive: "be sure to"'),
+    (r'\b[Pp]lease ensure\b', 'defensive-padding', 'Defensive: "please ensure"'),
+    (r'\b[Ii]t is important (?:to|that)\b', 'defensive-padding', 'Defensive: "it is important"'),
+    (r'\b[Yy]ou are an AI\b', 'meta-explanation', 'Meta: "you are an AI"'),
+    (r'\b[Aa]s a language model\b', 'meta-explanation', 'Meta: "as a language model"'),
+    (r'\b[Aa]s an AI assistant\b', 'meta-explanation', 'Meta: "as an AI assistant"'),
+    (r'\b[Tt]his (?:workflow|skill|process) is designed to\b', 'meta-explanation', 'Meta: "this workflow is designed to"'),
+    (r'\b[Tt]he purpose of this (?:section|step) is\b', 'meta-explanation', 'Meta: "the purpose of this section is"'),
+    (r"\b[Ll]et'?s (?:think about|begin|start)\b", 'filler', "Filler: \"let's think/begin\""),
+    (r'\b[Nn]ow we(?:\'ll| will)\b', 'filler', "Filler: \"now we'll\""),
+]
+
+# Back-reference patterns (self-containment risk)
+BACKREF_PATTERNS = [
+    (r'\bas described above\b', 'Back-reference: "as described above"'),
+    (r'\bper the overview\b', 'Back-reference: "per the overview"'),
+    (r'\bas mentioned (?:above|in|earlier)\b', 'Back-reference: "as mentioned above/in/earlier"'),
+    (r'\bsee (?:above|the overview)\b', 'Back-reference: "see above/the overview"'),
+    (r'\brefer to (?:the )?(?:above|overview|SKILL)\b', 'Back-reference: "refer to above/overview"'),
+]
+
+
+def count_tables(content: str) -> tuple[int, int]:
+    """Count markdown tables and their total lines."""
+    table_count = 0
+    table_lines = 0
+    in_table = False
+    for line in content.split('\n'):
+        if '|' in line and re.match(r'^\s*\|', line):
+            if not in_table:
+                table_count += 1
+                in_table = True
+            table_lines += 1
+        else:
+            in_table = False
+    return table_count, table_lines
+
+
+def count_fenced_blocks(content: str) -> tuple[int, int]:
+    """Count fenced code blocks and their total lines."""
+    block_count = 0
+    block_lines = 0
+    in_block = False
+    for line in content.split('\n'):
+        if line.strip().startswith('```'):
+            if in_block:
+                in_block = False
+            else:
+                in_block = True
+                block_count += 1
+        elif in_block:
+            block_lines += 1
+    return block_count, block_lines
+
+
+def extract_overview_size(content: str) -> int:
+    """Count lines in the ## Overview section."""
+    lines = content.split('\n')
+    in_overview = False
+    overview_lines = 0
+    for line in lines:
+        if re.match(r'^##\s+Overview\b', line):
+            in_overview = True
+            continue
+        elif in_overview and re.match(r'^##\s', line):
+            break
+        elif in_overview:
+            overview_lines += 1
+    return overview_lines
+
+
+def scan_file_patterns(filepath: Path, rel_path: str) -> dict:
+    """Extract metrics and pattern matches from a single file."""
+    content = filepath.read_text(encoding='utf-8')
+    lines = content.split('\n')
+    line_count = len(lines)
+
+    # Token estimate (rough: chars / 4)
+    token_estimate = len(content) // 4
+
+    # Section inventory
+    sections = []
+    for i, line in enumerate(lines, 1):
+        m = re.match(r'^(#{2,3})\s+(.+)$', line)
+        if m:
+            sections.append({'level': len(m.group(1)), 'title': m.group(2).strip(), 'line': i})
+
+    # Tables and code blocks
+    table_count, table_lines = count_tables(content)
+    block_count, block_lines = count_fenced_blocks(content)
+
+    # Pattern matches
+    waste_matches = []
+    for pattern, category, label in WASTE_PATTERNS:
+        for m in re.finditer(pattern, content):
+            line_num = content[:m.start()].count('\n') + 1
+            waste_matches.append({
+                'line': line_num,
+                'category': category,
+                'pattern': label,
+                'context': lines[line_num - 1].strip()[:100],
+            })
+
+    backref_matches = []
+    for pattern, label in BACKREF_PATTERNS:
+        for m in re.finditer(pattern, content, re.IGNORECASE):
+            line_num = content[:m.start()].count('\n') + 1
+            backref_matches.append({
+                'line': line_num,
+                'pattern': label,
+                'context': lines[line_num - 1].strip()[:100],
+            })
+
+    # Config header
+    has_config_header = '{communication_language}' in content or '{document_output_language}' in content
+
+    # Progression condition
+    prog_keywords = ['progress', 'advance', 'move to', 'next stage',
+                     'when complete', 'proceed to', 'transition', 'completion criteria']
+    has_progression = any(kw in content.lower() for kw in prog_keywords)
+
+    result = {
+        'file': rel_path,
+        'line_count': line_count,
+        'token_estimate': token_estimate,
+        'sections': sections,
+        'table_count': table_count,
+        'table_lines': table_lines,
+        'fenced_block_count': block_count,
+        'fenced_block_lines': block_lines,
+        'waste_patterns': waste_matches,
+        'back_references': backref_matches,
+        'has_config_header': has_config_header,
+        'has_progression': has_progression,
+    }
+
+    return result
+
+
+def scan_prompt_metrics(skill_path: Path) -> dict:
+    """Extract metrics from all prompt-relevant files."""
+    files_data = []
+
+    # SKILL.md
+    skill_md = skill_path / 'SKILL.md'
+    if skill_md.exists():
+        data = scan_file_patterns(skill_md, 'SKILL.md')
+        content = skill_md.read_text(encoding='utf-8')
+        data['overview_lines'] = extract_overview_size(content)
+        data['is_skill_md'] = True
+        files_data.append(data)
+
+    # Prompt files at skill root (non-SKILL.md .md files)
+    for f in sorted(skill_path.iterdir()):
+        if f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md':
+            data = scan_file_patterns(f, f.name)
+            data['is_skill_md'] = False
+            files_data.append(data)
+
+    # Resources (just sizes, for progressive disclosure assessment)
+    resources_dir = skill_path / 'resources'
+    resource_sizes = {}
+    if resources_dir.exists():
+        for f in sorted(resources_dir.iterdir()):
+            if f.is_file() and f.suffix in ('.md', '.json', '.yaml', '.yml'):
+                content = f.read_text(encoding='utf-8')
+                resource_sizes[f.name] = {
+                    'lines': len(content.split('\n')),
+                    'tokens': len(content) // 4,
+                }
+
+    # Aggregate stats
+    total_waste = sum(len(f['waste_patterns']) for f in files_data)
+    total_backrefs = sum(len(f['back_references']) for f in files_data)
+    total_tokens = sum(f['token_estimate'] for f in files_data)
+    prompts_with_config = sum(1 for f in files_data if not f.get('is_skill_md') and f['has_config_header'])
+    prompts_with_progression = sum(1 for f in files_data if not f.get('is_skill_md') and f['has_progression'])
+    total_prompts = sum(1 for f in files_data if not f.get('is_skill_md'))
+
+    skill_md_data = next((f for f in files_data if f.get('is_skill_md')), None)
+
+    return {
+        'scanner': 'prompt-craft-prepass',
+        'script': 'prepass-prompt-metrics.py',
+        'version': '1.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': 'info',
+        'skill_md_summary': {
+            'line_count': skill_md_data['line_count'] if skill_md_data else 0,
+            'token_estimate': skill_md_data['token_estimate'] if skill_md_data else 0,
+            'overview_lines': skill_md_data.get('overview_lines', 0) if skill_md_data else 0,
+            'table_count': skill_md_data['table_count'] if skill_md_data else 0,
+            'table_lines': skill_md_data['table_lines'] if skill_md_data else 0,
+            'fenced_block_count': skill_md_data['fenced_block_count'] if skill_md_data else 0,
+            'fenced_block_lines': skill_md_data['fenced_block_lines'] if skill_md_data else 0,
+            'section_count': len(skill_md_data['sections']) if skill_md_data else 0,
+        },
+        'prompt_health': {
+            'total_prompts': total_prompts,
+            'prompts_with_config_header': prompts_with_config,
+            'prompts_with_progression': prompts_with_progression,
+        },
+        'aggregate': {
+            'total_files_scanned': len(files_data),
+            'total_token_estimate': total_tokens,
+            'total_waste_patterns': total_waste,
+            'total_back_references': total_backrefs,
+        },
+        'resource_sizes': resource_sizes,
+        'files': files_data,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Extract prompt craft metrics for LLM scanner pre-pass',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_prompt_metrics(args.skill_path)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

.agent/skills/bmad-workflow-builder/scripts/prepass-workflow-integrity.py

@@ -0,0 +1,480 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for workflow integrity scanner.
+
+Extracts structural metadata from a BMad skill that the LLM scanner
+can use instead of reading all files itself. Covers:
+- Frontmatter parsing and validation
+- Section inventory (H2/H3 headers)
+- Template artifact detection
+- Stage file cross-referencing
+- Stage numbering validation
+- Config header detection in prompts
+- Language/directness pattern grep
+- On Exit / Exiting section detection (invalid)
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+# Template artifacts that should NOT appear in finalized skills
+TEMPLATE_ARTIFACTS = [
+    r'\{if-complex-workflow\}', r'\{/if-complex-workflow\}',
+    r'\{if-simple-workflow\}', r'\{/if-simple-workflow\}',
+    r'\{if-simple-utility\}', r'\{/if-simple-utility\}',
+    r'\{if-module\}', r'\{/if-module\}',
+    r'\{if-headless\}', r'\{/if-headless\}',
+    r'\{displayName\}', r'\{skillName\}',
+]
+# Runtime variables that ARE expected (not artifacts)
+RUNTIME_VARS = {
+    '{user_name}', '{communication_language}', '{document_output_language}',
+    '{project-root}', '{output_folder}', '{planning_artifacts}',
+}
+
+# Directness anti-patterns
+DIRECTNESS_PATTERNS = [
+    (r'\byou should\b', 'Suggestive "you should" — use direct imperative'),
+    (r'\bplease\b(?! note)', 'Polite "please" — use direct imperative'),
+    (r'\bhandle appropriately\b', 'Ambiguous "handle appropriately" — specify how'),
+    (r'\bwhen ready\b', 'Vague "when ready" — specify testable condition'),
+]
+
+# Invalid sections
+INVALID_SECTIONS = [
+    (r'^##\s+On\s+Exit\b', 'On Exit section found — no exit hooks exist in the system, this will never run'),
+    (r'^##\s+Exiting\b', 'Exiting section found — no exit hooks exist in the system, this will never run'),
+]
+
+
+def parse_frontmatter(content: str) -> tuple[dict | None, list[dict]]:
+    """Parse YAML frontmatter and validate."""
+    findings = []
+    fm_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL)
+    if not fm_match:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'critical', 'category': 'frontmatter',
+            'issue': 'No YAML frontmatter found',
+        })
+        return None, findings
+
+    try:
+        # Frontmatter is YAML-like key: value pairs — parse manually
+        fm = {}
+        for line in fm_match.group(1).strip().split('\n'):
+            line = line.strip()
+            if not line or line.startswith('#'):
+                continue
+            if ':' in line:
+                key, _, value = line.partition(':')
+                fm[key.strip()] = value.strip().strip('"').strip("'")
+    except Exception as e:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'critical', 'category': 'frontmatter',
+            'issue': f'Invalid frontmatter: {e}',
+        })
+        return None, findings
+
+    if not isinstance(fm, dict):
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'critical', 'category': 'frontmatter',
+            'issue': 'Frontmatter is not a YAML mapping',
+        })
+        return None, findings
+
+    # name check
+    name = fm.get('name')
+    if not name:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'critical', 'category': 'frontmatter',
+            'issue': 'Missing "name" field in frontmatter',
+        })
+    elif not re.match(r'^[a-z0-9]+(-[a-z0-9]+)*$', name):
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'frontmatter',
+            'issue': f'Name "{name}" is not kebab-case',
+        })
+    elif not name.startswith('bmad-'):
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'medium', 'category': 'frontmatter',
+            'issue': f'Name "{name}" does not follow bmad-* naming convention',
+        })
+
+    # description check
+    desc = fm.get('description')
+    if not desc:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'frontmatter',
+            'issue': 'Missing "description" field in frontmatter',
+        })
+    elif 'Use when' not in desc and 'use when' not in desc:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'medium', 'category': 'frontmatter',
+            'issue': 'Description missing "Use when..." trigger phrase',
+        })
+
+    # Extra fields check
+    allowed = {'name', 'description', 'menu-code'}
+    extra = set(fm.keys()) - allowed
+    if extra:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'low', 'category': 'frontmatter',
+            'issue': f'Extra frontmatter fields: {", ".join(sorted(extra))}',
+        })
+
+    return fm, findings
+
+
+def extract_sections(content: str) -> list[dict]:
+    """Extract all H2 headers with line numbers."""
+    sections = []
+    for i, line in enumerate(content.split('\n'), 1):
+        m = re.match(r'^(#{2,3})\s+(.+)$', line)
+        if m:
+            sections.append({
+                'level': len(m.group(1)),
+                'title': m.group(2).strip(),
+                'line': i,
+            })
+    return sections
+
+
+def check_required_sections(sections: list[dict]) -> list[dict]:
+    """Check for required and invalid sections."""
+    findings = []
+    h2_titles = [s['title'] for s in sections if s['level'] == 2]
+
+    if 'Overview' not in h2_titles:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'sections',
+            'issue': 'Missing ## Overview section',
+        })
+
+    if 'On Activation' not in h2_titles:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'sections',
+            'issue': 'Missing ## On Activation section',
+        })
+
+    # Invalid sections
+    for s in sections:
+        if s['level'] == 2:
+            for pattern, message in INVALID_SECTIONS:
+                if re.match(pattern, f"## {s['title']}"):
+                    findings.append({
+                        'file': 'SKILL.md', 'line': s['line'],
+                        'severity': 'high', 'category': 'invalid-section',
+                        'issue': message,
+                    })
+
+    return findings
+
+
+def find_template_artifacts(filepath: Path, rel_path: str) -> list[dict]:
+    """Scan for orphaned template substitution artifacts."""
+    findings = []
+    content = filepath.read_text(encoding='utf-8')
+
+    for pattern in TEMPLATE_ARTIFACTS:
+        for m in re.finditer(pattern, content):
+            matched = m.group()
+            if matched in RUNTIME_VARS:
+                continue
+            line_num = content[:m.start()].count('\n') + 1
+            findings.append({
+                'file': rel_path, 'line': line_num,
+                'severity': 'high', 'category': 'artifacts',
+                'issue': f'Orphaned template artifact: {matched}',
+                'fix': 'Resolve or remove this template conditional/placeholder',
+            })
+
+    return findings
+
+
+def cross_reference_stages(skill_path: Path, skill_content: str) -> tuple[dict, list[dict]]:
+    """Cross-reference stage files between SKILL.md and numbered prompt files at skill root."""
+    findings = []
+
+    # Get actual numbered prompt files at skill root (exclude SKILL.md)
+    actual_files = set()
+    for f in skill_path.iterdir():
+        if f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md' and re.match(r'^\d+-', f.name):
+            actual_files.add(f.name)
+
+    # Find stage references in SKILL.md — look for both old prompts/ style and new root style
+    referenced = set()
+    # Match `prompts/XX-name.md` (legacy) or bare `XX-name.md` references
+    ref_pattern = re.compile(r'(?:prompts/)?(\d+-[^\s)`]+\.md)')
+    for m in ref_pattern.finditer(skill_content):
+        referenced.add(m.group(1))
+
+    # Missing files (referenced but don't exist)
+    missing = referenced - actual_files
+    for f in sorted(missing):
+        findings.append({
+            'file': 'SKILL.md', 'line': 0,
+            'severity': 'critical', 'category': 'missing-stage',
+            'issue': f'Referenced stage file does not exist: {f}',
+        })
+
+    # Orphaned files (exist but not referenced)
+    orphaned = actual_files - referenced
+    for f in sorted(orphaned):
+        findings.append({
+            'file': f, 'line': 0,
+            'severity': 'medium', 'category': 'naming',
+            'issue': f'Stage file exists but not referenced in SKILL.md: {f}',
+        })
+
+    # Stage numbering check
+    numbered = []
+    for f in sorted(actual_files):
+        m = re.match(r'^(\d+)-(.+)\.md$', f)
+        if m:
+            numbered.append((int(m.group(1)), f))
+
+    if numbered:
+        numbered.sort()
+        nums = [n[0] for n in numbered]
+        expected = list(range(nums[0], nums[0] + len(nums)))
+        if nums != expected:
+            gaps = set(expected) - set(nums)
+            if gaps:
+                findings.append({
+                    'file': skill_path.name, 'line': 0,
+                    'severity': 'medium', 'category': 'naming',
+                    'issue': f'Stage numbering has gaps: missing {sorted(gaps)}',
+                })
+
+    stage_summary = {
+        'total_stages': len(actual_files),
+        'referenced': sorted(referenced),
+        'actual': sorted(actual_files),
+        'missing_stages': sorted(missing),
+        'orphaned_stages': sorted(orphaned),
+    }
+
+    return stage_summary, findings
+
+
+def check_prompt_basics(skill_path: Path) -> tuple[list[dict], list[dict]]:
+    """Check each prompt file for config header and progression conditions."""
+    findings = []
+    prompt_details = []
+
+    # Look for numbered prompt files at skill root
+    prompt_files = sorted(
+        f for f in skill_path.iterdir()
+        if f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md' and re.match(r'^\d+-', f.name)
+    )
+    if not prompt_files:
+        return prompt_details, findings
+
+    for f in prompt_files:
+        content = f.read_text(encoding='utf-8')
+        rel_path = f.name
+        detail = {'file': f.name, 'has_config_header': False, 'has_progression': False}
+
+        # Config header check
+        if '{communication_language}' in content or '{document_output_language}' in content:
+            detail['has_config_header'] = True
+        else:
+            findings.append({
+                'file': rel_path, 'line': 1,
+                'severity': 'medium', 'category': 'config-header',
+                'issue': 'No config header with language variables found',
+            })
+
+        # Progression condition check (look for progression-related keywords near end)
+        lower = content.lower()
+        prog_keywords = ['progress', 'advance', 'move to', 'next stage', 'when complete',
+                         'proceed to', 'transition', 'completion criteria']
+        if any(kw in lower for kw in prog_keywords):
+            detail['has_progression'] = True
+        else:
+            findings.append({
+                'file': rel_path, 'line': len(content.split('\n')),
+                'severity': 'high', 'category': 'progression',
+                'issue': 'No progression condition keywords found',
+            })
+
+        # Directness checks
+        for pattern, message in DIRECTNESS_PATTERNS:
+            for m in re.finditer(pattern, content, re.IGNORECASE):
+                line_num = content[:m.start()].count('\n') + 1
+                findings.append({
+                    'file': rel_path, 'line': line_num,
+                    'severity': 'low', 'category': 'language',
+                    'issue': message,
+                })
+
+        # Template artifacts
+        findings.extend(find_template_artifacts(f, rel_path))
+
+        prompt_details.append(detail)
+
+    return prompt_details, findings
+
+
+def detect_workflow_type(skill_content: str, has_prompts: bool) -> str:
+    """Detect workflow type from SKILL.md content."""
+    has_stage_refs = bool(re.search(r'(?:prompts/)?\d+-\S+\.md', skill_content))
+    has_routing = bool(re.search(r'(?i)(rout|stage|branch|path)', skill_content))
+
+    if has_stage_refs or (has_prompts and has_routing):
+        return 'complex'
+    elif re.search(r'(?m)^\d+\.\s', skill_content):
+        return 'simple-workflow'
+    else:
+        return 'simple-utility'
+
+
+def scan_workflow_integrity(skill_path: Path) -> dict:
+    """Run all deterministic workflow integrity checks."""
+    all_findings = []
+
+    # Read SKILL.md
+    skill_md = skill_path / 'SKILL.md'
+    if not skill_md.exists():
+        return {
+            'scanner': 'workflow-integrity-prepass',
+            'script': 'prepass-workflow-integrity.py',
+            'version': '1.0.0',
+            'skill_path': str(skill_path),
+            'timestamp': datetime.now(timezone.utc).isoformat(),
+            'status': 'fail',
+            'issues': [{'file': 'SKILL.md', 'line': 1, 'severity': 'critical',
+                         'category': 'missing-file', 'issue': 'SKILL.md does not exist'}],
+            'summary': {'total_issues': 1, 'by_severity': {'critical': 1, 'high': 0, 'medium': 0, 'low': 0}},
+        }
+
+    skill_content = skill_md.read_text(encoding='utf-8')
+
+    # Frontmatter
+    frontmatter, fm_findings = parse_frontmatter(skill_content)
+    all_findings.extend(fm_findings)
+
+    # Sections
+    sections = extract_sections(skill_content)
+    section_findings = check_required_sections(sections)
+    all_findings.extend(section_findings)
+
+    # Template artifacts in SKILL.md
+    all_findings.extend(find_template_artifacts(skill_md, 'SKILL.md'))
+
+    # Directness checks in SKILL.md
+    for pattern, message in DIRECTNESS_PATTERNS:
+        for m in re.finditer(pattern, skill_content, re.IGNORECASE):
+            line_num = skill_content[:m.start()].count('\n') + 1
+            all_findings.append({
+                'file': 'SKILL.md', 'line': line_num,
+                'severity': 'low', 'category': 'language',
+                'issue': message,
+            })
+
+    # Workflow type
+    has_prompts = any(
+        f.is_file() and f.suffix == '.md' and f.name != 'SKILL.md' and re.match(r'^\d+-', f.name)
+        for f in skill_path.iterdir()
+    )
+    workflow_type = detect_workflow_type(skill_content, has_prompts)
+
+    # Stage cross-reference
+    stage_summary, stage_findings = cross_reference_stages(skill_path, skill_content)
+    all_findings.extend(stage_findings)
+
+    # Prompt basics
+    prompt_details, prompt_findings = check_prompt_basics(skill_path)
+    all_findings.extend(prompt_findings)
+
+    # Build severity summary
+    by_severity = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0}
+    for f in all_findings:
+        sev = f['severity']
+        if sev in by_severity:
+            by_severity[sev] += 1
+
+    status = 'pass'
+    if by_severity['critical'] > 0:
+        status = 'fail'
+    elif by_severity['high'] > 0:
+        status = 'warning'
+
+    return {
+        'scanner': 'workflow-integrity-prepass',
+        'script': 'prepass-workflow-integrity.py',
+        'version': '1.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': status,
+        'metadata': {
+            'frontmatter': frontmatter,
+            'sections': sections,
+            'workflow_type': workflow_type,
+        },
+        'stage_summary': stage_summary,
+        'prompt_details': prompt_details,
+        'issues': all_findings,
+        'summary': {
+            'total_issues': len(all_findings),
+            'by_severity': by_severity,
+        },
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Deterministic pre-pass for workflow integrity scanning',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_workflow_integrity(args.skill_path)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0 if result['status'] == 'pass' else 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())

.agent/skills/bmad-workflow-builder/scripts/scan-path-standards.py

@@ -0,0 +1,300 @@
+#!/usr/bin/env python3
+"""Deterministic path standards scanner for BMad skills.
+
+Validates all .md and .json files against BMad path conventions:
+1. {project-root} only valid before /_bmad
+2. Bare _bmad references must have {project-root} prefix
+3. Config variables used directly (no double-prefix)
+4. Skill-internal paths must use ./ prefix (references/, scripts/, assets/)
+5. No ../ parent directory references
+6. No absolute paths
+7. Frontmatter allows only name and description
+8. No .md files at skill root except SKILL.md
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+# Patterns to detect
+# {project-root} NOT followed by /_bmad
+PROJECT_ROOT_NOT_BMAD_RE = re.compile(r'\{project-root\}/(?!_bmad)')
+# Bare _bmad without {project-root} prefix — match _bmad at word boundary
+# but not when preceded by {project-root}/
+BARE_BMAD_RE = re.compile(r'(?<!\{project-root\}/)_bmad[/\s]')
+# Absolute paths
+ABSOLUTE_PATH_RE = re.compile(r'(?:^|[\s"`\'(])(/(?:Users|home|opt|var|tmp|etc|usr)/\S+)', re.MULTILINE)
+HOME_PATH_RE = re.compile(r'(?:^|[\s"`\'(])(~/\S+)', re.MULTILINE)
+# Parent directory reference (still invalid)
+RELATIVE_DOT_RE = re.compile(r'(?:^|[\s"`\'(])(\.\./\S+)', re.MULTILINE)
+# Bare skill-internal paths without ./ prefix
+# Match references/, scripts/, assets/ when NOT preceded by ./
+BARE_INTERNAL_RE = re.compile(r'(?:^|[\s"`\'(])(?<!\./)((?:references|scripts|assets)/\S+)', re.MULTILINE)
+
+# Fenced code block detection (to skip examples showing wrong patterns)
+FENCE_RE = re.compile(r'^```', re.MULTILINE)
+
+# Valid frontmatter keys
+VALID_FRONTMATTER_KEYS = {'name', 'description'}
+
+
+def is_in_fenced_block(content: str, pos: int) -> bool:
+    """Check if a position is inside a fenced code block."""
+    fences = [m.start() for m in FENCE_RE.finditer(content[:pos])]
+    # Odd number of fences before pos means we're inside a block
+    return len(fences) % 2 == 1
+
+
+def get_line_number(content: str, pos: int) -> int:
+    """Get 1-based line number for a position in content."""
+    return content[:pos].count('\n') + 1
+
+
+def check_frontmatter(content: str, filepath: Path) -> list[dict]:
+    """Validate SKILL.md frontmatter contains only allowed keys."""
+    findings = []
+    if filepath.name != 'SKILL.md':
+        return findings
+
+    if not content.startswith('---'):
+        findings.append({
+            'file': filepath.name,
+            'line': 1,
+            'severity': 'critical',
+            'category': 'frontmatter',
+            'title': 'SKILL.md missing frontmatter block',
+            'detail': 'SKILL.md must start with --- frontmatter containing name and description',
+            'action': 'Add frontmatter with name and description fields',
+        })
+        return findings
+
+    # Find closing ---
+    end = content.find('\n---', 3)
+    if end == -1:
+        findings.append({
+            'file': filepath.name,
+            'line': 1,
+            'severity': 'critical',
+            'category': 'frontmatter',
+            'title': 'SKILL.md frontmatter block not closed',
+            'detail': 'Missing closing --- for frontmatter',
+            'action': 'Add closing --- after frontmatter fields',
+        })
+        return findings
+
+    frontmatter = content[4:end]
+    for i, line in enumerate(frontmatter.split('\n'), start=2):
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+        if ':' in line:
+            key = line.split(':', 1)[0].strip()
+            if key not in VALID_FRONTMATTER_KEYS:
+                findings.append({
+                    'file': filepath.name,
+                    'line': i,
+                    'severity': 'high',
+                    'category': 'frontmatter',
+                    'title': f'Invalid frontmatter key: {key}',
+                    'detail': f'Only {", ".join(sorted(VALID_FRONTMATTER_KEYS))} are allowed in frontmatter',
+                    'action': f'Remove {key} from frontmatter — use as content field in SKILL.md body instead',
+                })
+
+    return findings
+
+
+def check_root_md_files(skill_path: Path) -> list[dict]:
+    """Check that no .md files exist at skill root except SKILL.md."""
+    findings = []
+    for md_file in skill_path.glob('*.md'):
+        if md_file.name != 'SKILL.md':
+            findings.append({
+                'file': md_file.name,
+                'line': 0,
+                'severity': 'high',
+                'category': 'structure',
+                'title': f'Prompt file at skill root: {md_file.name}',
+                'detail': 'All progressive disclosure content must be in ./references/ — only SKILL.md belongs at root',
+                'action': f'Move {md_file.name} to references/{md_file.name}',
+            })
+    return findings
+
+
+def scan_file(filepath: Path, skip_fenced: bool = True) -> list[dict]:
+    """Scan a single file for path standard violations."""
+    findings = []
+    content = filepath.read_text(encoding='utf-8')
+    rel_path = filepath.name
+
+    checks = [
+        (PROJECT_ROOT_NOT_BMAD_RE, 'project-root-not-bmad', 'critical',
+         '{project-root} used for non-_bmad path — only valid use is {project-root}/_bmad/...'),
+        (ABSOLUTE_PATH_RE, 'absolute-path', 'high',
+         'Absolute path found — not portable across machines'),
+        (HOME_PATH_RE, 'absolute-path', 'high',
+         'Home directory path (~/) found — environment-specific'),
+        (RELATIVE_DOT_RE, 'relative-prefix', 'high',
+         'Parent directory reference (../) found — fragile, breaks with reorganization'),
+        (BARE_INTERNAL_RE, 'bare-internal-path', 'high',
+         'Bare skill-internal path without ./ prefix — use ./references/, ./scripts/, ./assets/ to distinguish from {project-root} paths'),
+    ]
+
+    for pattern, category, severity, message in checks:
+        for match in pattern.finditer(content):
+            pos = match.start()
+            if skip_fenced and is_in_fenced_block(content, pos):
+                continue
+            line_num = get_line_number(content, pos)
+            line_content = content.split('\n')[line_num - 1].strip()
+            findings.append({
+                'file': rel_path,
+                'line': line_num,
+                'severity': severity,
+                'category': category,
+                'title': message,
+                'detail': line_content[:120],
+                'action': '',
+            })
+
+    # Bare _bmad check — more nuanced, need to avoid false positives
+    # inside {project-root}/_bmad which is correct
+    for match in BARE_BMAD_RE.finditer(content):
+        pos = match.start()
+        if skip_fenced and is_in_fenced_block(content, pos):
+            continue
+        start = max(0, pos - 30)
+        before = content[start:pos]
+        if '{project-root}/' in before:
+            continue
+        line_num = get_line_number(content, pos)
+        line_content = content.split('\n')[line_num - 1].strip()
+        findings.append({
+            'file': rel_path,
+            'line': line_num,
+            'severity': 'high',
+            'category': 'bare-bmad',
+            'title': 'Bare _bmad reference without {project-root} prefix',
+            'detail': line_content[:120],
+            'action': '',
+        })
+
+    return findings
+
+
+def scan_skill(skill_path: Path, skip_fenced: bool = True) -> dict:
+    """Scan all .md and .json files in a skill directory."""
+    all_findings = []
+
+    # Check for .md files at root that aren't SKILL.md
+    all_findings.extend(check_root_md_files(skill_path))
+
+    # Check SKILL.md frontmatter
+    skill_md = skill_path / 'SKILL.md'
+    if skill_md.exists():
+        content = skill_md.read_text(encoding='utf-8')
+        all_findings.extend(check_frontmatter(content, skill_md))
+
+    # Find all .md and .json files
+    md_files = sorted(list(skill_path.rglob('*.md')) + list(skill_path.rglob('*.json')))
+    if not md_files:
+        print(f"Warning: No .md or .json files found in {skill_path}", file=sys.stderr)
+
+    files_scanned = []
+    for md_file in md_files:
+        rel = md_file.relative_to(skill_path)
+        files_scanned.append(str(rel))
+        file_findings = scan_file(md_file, skip_fenced)
+        for f in file_findings:
+            f['file'] = str(rel)
+        all_findings.extend(file_findings)
+
+    # Build summary
+    by_severity = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0}
+    by_category = {
+        'project_root_not_bmad': 0,
+        'bare_bmad': 0,
+        'double_prefix': 0,
+        'absolute_path': 0,
+        'relative_prefix': 0,
+        'bare_internal_path': 0,
+        'frontmatter': 0,
+        'structure': 0,
+    }
+
+    for f in all_findings:
+        sev = f['severity']
+        if sev in by_severity:
+            by_severity[sev] += 1
+        cat = f['category'].replace('-', '_')
+        if cat in by_category:
+            by_category[cat] += 1
+
+    return {
+        'scanner': 'path-standards',
+        'script': 'scan-path-standards.py',
+        'version': '2.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'files_scanned': files_scanned,
+        'status': 'pass' if not all_findings else 'fail',
+        'findings': all_findings,
+        'assessments': {},
+        'summary': {
+            'total_findings': len(all_findings),
+            'by_severity': by_severity,
+            'by_category': by_category,
+            'assessment': 'Path standards scan complete',
+        },
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Scan BMad skill for path standard violations',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    parser.add_argument(
+        '--include-fenced',
+        action='store_true',
+        help='Also check inside fenced code blocks (by default they are skipped)',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_skill(args.skill_path, skip_fenced=not args.include_fenced)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0 if result['status'] == 'pass' else 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())

.agent/skills/bmad-workflow-builder/scripts/scan-scripts.py

@@ -0,0 +1,745 @@
+#!/usr/bin/env python3
+"""Deterministic scripts scanner for BMad skills.
+
+Validates scripts in a skill's scripts/ folder for:
+- PEP 723 inline dependencies (Python)
+- Shebang, set -e, portability (Shell)
+- Version pinning for npx/uvx
+- Agentic design: no input(), has argparse/--help, JSON output, exit codes
+- Unit test existence
+- Over-engineering signals (line count, simple-op imports)
+- External lint: ruff (Python), shellcheck (Bash), biome (JS/TS)
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import ast
+import json
+import re
+import shutil
+import subprocess
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+# =============================================================================
+# External Linter Integration
+# =============================================================================
+
+def _run_command(cmd: list[str], timeout: int = 30) -> tuple[int, str, str]:
+    """Run a command and return (returncode, stdout, stderr)."""
+    try:
+        result = subprocess.run(
+            cmd, capture_output=True, text=True, timeout=timeout,
+        )
+        return result.returncode, result.stdout, result.stderr
+    except FileNotFoundError:
+        return -1, '', f'Command not found: {cmd[0]}'
+    except subprocess.TimeoutExpired:
+        return -2, '', f'Command timed out after {timeout}s: {" ".join(cmd)}'
+
+
+def _find_uv() -> str | None:
+    """Find uv binary on PATH."""
+    return shutil.which('uv')
+
+
+def _find_npx() -> str | None:
+    """Find npx binary on PATH."""
+    return shutil.which('npx')
+
+
+def lint_python_ruff(filepath: Path, rel_path: str) -> list[dict]:
+    """Run ruff on a Python file via uv. Returns lint findings."""
+    uv = _find_uv()
+    if not uv:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': 'uv not found on PATH — cannot run ruff for Python linting',
+            'detail': '',
+            'action': 'Install uv: https://docs.astral.sh/uv/getting-started/installation/',
+        }]
+
+    rc, stdout, stderr = _run_command([
+        uv, 'run', 'ruff', 'check', '--output-format', 'json', str(filepath),
+    ])
+
+    if rc == -1:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': f'Failed to run ruff via uv: {stderr.strip()}',
+            'detail': '',
+            'action': 'Ensure uv can install and run ruff: uv run ruff --version',
+        }]
+
+    if rc == -2:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'ruff timed out on {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    # ruff outputs JSON array on stdout (even on rc=1 when issues found)
+    findings = []
+    try:
+        issues = json.loads(stdout) if stdout.strip() else []
+    except json.JSONDecodeError:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'Failed to parse ruff output for {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    for issue in issues:
+        fix_msg = issue.get('fix', {}).get('message', '') if issue.get('fix') else ''
+        findings.append({
+            'file': rel_path,
+            'line': issue.get('location', {}).get('row', 0),
+            'severity': 'high',
+            'category': 'lint',
+            'title': f'[{issue.get("code", "?")}] {issue.get("message", "")}',
+            'detail': '',
+            'action': fix_msg or f'See https://docs.astral.sh/ruff/rules/{issue.get("code", "")}',
+        })
+
+    return findings
+
+
+def lint_shell_shellcheck(filepath: Path, rel_path: str) -> list[dict]:
+    """Run shellcheck on a shell script via uv. Returns lint findings."""
+    uv = _find_uv()
+    if not uv:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': 'uv not found on PATH — cannot run shellcheck for shell linting',
+            'detail': '',
+            'action': 'Install uv: https://docs.astral.sh/uv/getting-started/installation/',
+        }]
+
+    rc, stdout, stderr = _run_command([
+        uv, 'run', '--with', 'shellcheck-py',
+        'shellcheck', '--format', 'json', str(filepath),
+    ])
+
+    if rc == -1:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': f'Failed to run shellcheck via uv: {stderr.strip()}',
+            'detail': '',
+            'action': 'Ensure uv can install shellcheck-py: uv run --with shellcheck-py shellcheck --version',
+        }]
+
+    if rc == -2:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'shellcheck timed out on {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    findings = []
+    # shellcheck outputs JSON on stdout (rc=1 when issues found)
+    raw = stdout.strip() or stderr.strip()
+    try:
+        issues = json.loads(raw) if raw else []
+    except json.JSONDecodeError:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'Failed to parse shellcheck output for {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    # Map shellcheck levels to our severity
+    level_map = {'error': 'high', 'warning': 'high', 'info': 'high', 'style': 'medium'}
+
+    for issue in issues:
+        sc_code = issue.get('code', '')
+        findings.append({
+            'file': rel_path,
+            'line': issue.get('line', 0),
+            'severity': level_map.get(issue.get('level', ''), 'high'),
+            'category': 'lint',
+            'title': f'[SC{sc_code}] {issue.get("message", "")}',
+            'detail': '',
+            'action': f'See https://www.shellcheck.net/wiki/SC{sc_code}',
+        })
+
+    return findings
+
+
+def lint_node_biome(filepath: Path, rel_path: str) -> list[dict]:
+    """Run biome on a JS/TS file via npx. Returns lint findings."""
+    npx = _find_npx()
+    if not npx:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': 'npx not found on PATH — cannot run biome for JS/TS linting',
+            'detail': '',
+            'action': 'Install Node.js 20+: https://nodejs.org/',
+        }]
+
+    rc, stdout, stderr = _run_command([
+        npx, '--yes', '@biomejs/biome', 'lint', '--reporter', 'json', str(filepath),
+    ], timeout=60)
+
+    if rc == -1:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'high', 'category': 'lint-setup',
+            'title': f'Failed to run biome via npx: {stderr.strip()}',
+            'detail': '',
+            'action': 'Ensure npx can run biome: npx @biomejs/biome --version',
+        }]
+
+    if rc == -2:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'biome timed out on {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    findings = []
+    # biome outputs JSON on stdout
+    raw = stdout.strip()
+    try:
+        result = json.loads(raw) if raw else {}
+    except json.JSONDecodeError:
+        return [{
+            'file': rel_path, 'line': 0,
+            'severity': 'medium', 'category': 'lint',
+            'title': f'Failed to parse biome output for {rel_path}',
+            'detail': '',
+            'action': '',
+        }]
+
+    for diag in result.get('diagnostics', []):
+        loc = diag.get('location', {})
+        start = loc.get('start', {})
+        findings.append({
+            'file': rel_path,
+            'line': start.get('line', 0),
+            'severity': 'high',
+            'category': 'lint',
+            'title': f'[{diag.get("category", "?")}] {diag.get("message", "")}',
+            'detail': '',
+            'action': diag.get('advices', [{}])[0].get('message', '') if diag.get('advices') else '',
+        })
+
+    return findings
+
+
+# =============================================================================
+# BMad Pattern Checks (Existing)
+# =============================================================================
+
+def scan_python_script(filepath: Path, rel_path: str) -> list[dict]:
+    """Check a Python script for standards compliance."""
+    findings = []
+    content = filepath.read_text(encoding='utf-8')
+    lines = content.split('\n')
+    line_count = len(lines)
+
+    # PEP 723 check
+    if '# /// script' not in content:
+        # Only flag if the script has imports (not a trivial script)
+        if 'import ' in content:
+            findings.append({
+                'file': rel_path, 'line': 1,
+                'severity': 'medium', 'category': 'dependencies',
+                'title': 'No PEP 723 inline dependency block (# /// script)',
+                'detail': '',
+                'action': 'Add PEP 723 block with requires-python and dependencies',
+            })
+    else:
+        # Check requires-python is present
+        if 'requires-python' not in content:
+            findings.append({
+                'file': rel_path, 'line': 1,
+                'severity': 'low', 'category': 'dependencies',
+                'title': 'PEP 723 block exists but missing requires-python constraint',
+                'detail': '',
+                'action': 'Add requires-python = ">=3.9" or appropriate version',
+            })
+
+    # requirements.txt reference
+    if 'requirements.txt' in content or 'pip install' in content:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'high', 'category': 'dependencies',
+            'title': 'References requirements.txt or pip install — use PEP 723 inline deps',
+            'detail': '',
+            'action': 'Replace with PEP 723 inline dependency block',
+        })
+
+    # Agentic design checks via AST
+    try:
+        tree = ast.parse(content)
+    except SyntaxError:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'critical', 'category': 'error-handling',
+            'title': 'Python syntax error — script cannot be parsed',
+            'detail': '',
+            'action': '',
+        })
+        return findings
+
+    has_argparse = False
+    has_json_dumps = False
+    has_sys_exit = False
+    imports = set()
+
+    for node in ast.walk(tree):
+        # Track imports
+        if isinstance(node, ast.Import):
+            for alias in node.names:
+                imports.add(alias.name)
+        elif isinstance(node, ast.ImportFrom):
+            if node.module:
+                imports.add(node.module)
+
+        # input() calls
+        if isinstance(node, ast.Call):
+            func = node.func
+            if isinstance(func, ast.Name) and func.id == 'input':
+                findings.append({
+                    'file': rel_path, 'line': node.lineno,
+                    'severity': 'critical', 'category': 'agentic-design',
+                    'title': 'input() call found — blocks in non-interactive agent execution',
+                    'detail': '',
+                    'action': 'Use argparse with required flags instead of interactive prompts',
+                })
+            # json.dumps
+            if isinstance(func, ast.Attribute) and func.attr == 'dumps':
+                has_json_dumps = True
+            # sys.exit
+            if isinstance(func, ast.Attribute) and func.attr == 'exit':
+                has_sys_exit = True
+            if isinstance(func, ast.Name) and func.id == 'exit':
+                has_sys_exit = True
+
+        # argparse
+        if isinstance(node, ast.Attribute) and node.attr == 'ArgumentParser':
+            has_argparse = True
+
+    if not has_argparse and line_count > 20:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'agentic-design',
+            'title': 'No argparse found — script lacks --help self-documentation',
+            'detail': '',
+            'action': 'Add argparse with description and argument help text',
+        })
+
+    if not has_json_dumps and line_count > 20:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'agentic-design',
+            'title': 'No json.dumps found — output may not be structured JSON',
+            'detail': '',
+            'action': 'Use json.dumps for structured output parseable by workflows',
+        })
+
+    if not has_sys_exit and line_count > 20:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'low', 'category': 'agentic-design',
+            'title': 'No sys.exit() calls — may not return meaningful exit codes',
+            'detail': '',
+            'action': 'Return 0=success, 1=fail, 2=error via sys.exit()',
+        })
+
+    # Over-engineering: simple file ops in Python
+    simple_op_imports = {'shutil', 'glob', 'fnmatch'}
+    over_eng = imports & simple_op_imports
+    if over_eng and line_count < 30:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'low', 'category': 'over-engineered',
+            'title': f'Short script ({line_count} lines) imports {", ".join(over_eng)} — may be simpler as bash',
+            'detail': '',
+            'action': 'Consider if cp/mv/find shell commands would suffice',
+        })
+
+    # Very short script
+    if line_count < 5:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'over-engineered',
+            'title': f'Script is only {line_count} lines — could be an inline command',
+            'detail': '',
+            'action': 'Consider inlining this command directly in the prompt',
+        })
+
+    return findings
+
+
+def scan_shell_script(filepath: Path, rel_path: str) -> list[dict]:
+    """Check a shell script for standards compliance."""
+    findings = []
+    content = filepath.read_text(encoding='utf-8')
+    lines = content.split('\n')
+    line_count = len(lines)
+
+    # Shebang
+    if not lines[0].startswith('#!'):
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'high', 'category': 'portability',
+            'title': 'Missing shebang line',
+            'detail': '',
+            'action': 'Add #!/usr/bin/env bash or #!/usr/bin/env sh',
+        })
+    elif '/usr/bin/env' not in lines[0]:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'portability',
+            'title': f'Shebang uses hardcoded path: {lines[0].strip()}',
+            'detail': '',
+            'action': 'Use #!/usr/bin/env bash for cross-platform compatibility',
+        })
+
+    # set -e
+    if 'set -e' not in content and 'set -euo' not in content:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'error-handling',
+            'title': 'Missing set -e — errors will be silently ignored',
+            'detail': '',
+            'action': 'Add set -e (or set -euo pipefail) near the top',
+        })
+
+    # Hardcoded interpreter paths
+    hardcoded_re = re.compile(r'/usr/bin/(python|ruby|node|perl)\b')
+    for i, line in enumerate(lines, 1):
+        if hardcoded_re.search(line):
+            findings.append({
+                'file': rel_path, 'line': i,
+                'severity': 'medium', 'category': 'portability',
+                'title': f'Hardcoded interpreter path: {line.strip()}',
+                'detail': '',
+                'action': 'Use /usr/bin/env or PATH-based lookup',
+            })
+
+    # GNU-only tools
+    gnu_re = re.compile(r'\b(gsed|gawk|ggrep|gfind)\b')
+    for i, line in enumerate(lines, 1):
+        m = gnu_re.search(line)
+        if m:
+            findings.append({
+                'file': rel_path, 'line': i,
+                'severity': 'medium', 'category': 'portability',
+                'title': f'GNU-only tool: {m.group()} — not available on all platforms',
+                'detail': '',
+                'action': 'Use POSIX-compatible equivalent',
+            })
+
+    # Unquoted variables (basic check)
+    unquoted_re = re.compile(r'(?<!")\$\w+(?!")')
+    for i, line in enumerate(lines, 1):
+        if line.strip().startswith('#'):
+            continue
+        for m in unquoted_re.finditer(line):
+            # Skip inside double-quoted strings (rough heuristic)
+            before = line[:m.start()]
+            if before.count('"') % 2 == 1:
+                continue
+            findings.append({
+                'file': rel_path, 'line': i,
+                'severity': 'low', 'category': 'portability',
+                'title': f'Potentially unquoted variable: {m.group()} — breaks with spaces in paths',
+                'detail': '',
+                'action': f'Use "{m.group()}" with double quotes',
+            })
+
+    # npx/uvx without version pinning
+    no_pin_re = re.compile(r'\b(npx|uvx)\s+([a-zA-Z][\w-]+)(?!\S*@)')
+    for i, line in enumerate(lines, 1):
+        if line.strip().startswith('#'):
+            continue
+        m = no_pin_re.search(line)
+        if m:
+            findings.append({
+                'file': rel_path, 'line': i,
+                'severity': 'medium', 'category': 'dependencies',
+                'title': f'{m.group(1)} {m.group(2)} without version pinning',
+                'detail': '',
+                'action': f'Pin version: {m.group(1)} {m.group(2)}@<version>',
+            })
+
+    # Very short script
+    if line_count < 5:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'over-engineered',
+            'title': f'Script is only {line_count} lines — could be an inline command',
+            'detail': '',
+            'action': 'Consider inlining this command directly in the prompt',
+        })
+
+    return findings
+
+
+def scan_node_script(filepath: Path, rel_path: str) -> list[dict]:
+    """Check a JS/TS script for standards compliance."""
+    findings = []
+    content = filepath.read_text(encoding='utf-8')
+    lines = content.split('\n')
+    line_count = len(lines)
+
+    # npx/uvx without version pinning
+    no_pin = re.compile(r'\b(npx|uvx)\s+([a-zA-Z][\w-]+)(?!\S*@)')
+    for i, line in enumerate(lines, 1):
+        m = no_pin.search(line)
+        if m:
+            findings.append({
+                'file': rel_path, 'line': i,
+                'severity': 'medium', 'category': 'dependencies',
+                'title': f'{m.group(1)} {m.group(2)} without version pinning',
+                'detail': '',
+                'action': f'Pin version: {m.group(1)} {m.group(2)}@<version>',
+            })
+
+    # Very short script
+    if line_count < 5:
+        findings.append({
+            'file': rel_path, 'line': 1,
+            'severity': 'medium', 'category': 'over-engineered',
+            'title': f'Script is only {line_count} lines — could be an inline command',
+            'detail': '',
+            'action': 'Consider inlining this command directly in the prompt',
+        })
+
+    return findings
+
+
+# =============================================================================
+# Main Scanner
+# =============================================================================
+
+def scan_skill_scripts(skill_path: Path) -> dict:
+    """Scan all scripts in a skill directory."""
+    scripts_dir = skill_path / 'scripts'
+    all_findings = []
+    lint_findings = []
+    script_inventory = {'python': [], 'shell': [], 'node': [], 'other': []}
+    missing_tests = []
+
+    if not scripts_dir.exists():
+        return {
+            'scanner': 'scripts',
+            'script': 'scan-scripts.py',
+            'version': '2.0.0',
+            'skill_path': str(skill_path),
+            'timestamp': datetime.now(timezone.utc).isoformat(),
+            'status': 'pass',
+            'findings': [{
+                'file': 'scripts/',
+                'severity': 'info',
+                'category': 'none',
+                'title': 'No scripts/ directory found — nothing to scan',
+                'detail': '',
+                'action': '',
+            }],
+            'assessments': {
+                'lint_summary': {
+                    'tools_used': [],
+                    'files_linted': 0,
+                    'lint_issues': 0,
+                },
+                'script_summary': {
+                    'total_scripts': 0,
+                    'by_type': script_inventory,
+                    'missing_tests': [],
+                },
+            },
+            'summary': {
+                'total_findings': 0,
+                'by_severity': {'critical': 0, 'high': 0, 'medium': 0, 'low': 0},
+                'assessment': '',
+            },
+        }
+
+    # Find all script files (exclude tests/ and __pycache__)
+    script_files = []
+    for f in sorted(scripts_dir.iterdir()):
+        if f.is_file() and f.suffix in ('.py', '.sh', '.bash', '.js', '.ts', '.mjs'):
+            script_files.append(f)
+
+    tests_dir = scripts_dir / 'tests'
+    lint_tools_used = set()
+
+    for script_file in script_files:
+        rel_path = f'scripts/{script_file.name}'
+        ext = script_file.suffix
+
+        if ext == '.py':
+            script_inventory['python'].append(script_file.name)
+            findings = scan_python_script(script_file, rel_path)
+            lf = lint_python_ruff(script_file, rel_path)
+            lint_findings.extend(lf)
+            if lf and not any(f['category'] == 'lint-setup' for f in lf):
+                lint_tools_used.add('ruff')
+        elif ext in ('.sh', '.bash'):
+            script_inventory['shell'].append(script_file.name)
+            findings = scan_shell_script(script_file, rel_path)
+            lf = lint_shell_shellcheck(script_file, rel_path)
+            lint_findings.extend(lf)
+            if lf and not any(f['category'] == 'lint-setup' for f in lf):
+                lint_tools_used.add('shellcheck')
+        elif ext in ('.js', '.ts', '.mjs'):
+            script_inventory['node'].append(script_file.name)
+            findings = scan_node_script(script_file, rel_path)
+            lf = lint_node_biome(script_file, rel_path)
+            lint_findings.extend(lf)
+            if lf and not any(f['category'] == 'lint-setup' for f in lf):
+                lint_tools_used.add('biome')
+        else:
+            script_inventory['other'].append(script_file.name)
+            findings = []
+
+        # Check for unit tests
+        if tests_dir.exists():
+            stem = script_file.stem
+            test_patterns = [
+                f'test_{stem}{ext}', f'test-{stem}{ext}',
+                f'{stem}_test{ext}', f'{stem}-test{ext}',
+                f'test_{stem}.py', f'test-{stem}.py',
+            ]
+            has_test = any((tests_dir / t).exists() for t in test_patterns)
+        else:
+            has_test = False
+
+        if not has_test:
+            missing_tests.append(script_file.name)
+            findings.append({
+                'file': rel_path, 'line': 1,
+                'severity': 'medium', 'category': 'tests',
+                'title': f'No unit test found for {script_file.name}',
+                'detail': '',
+                'action': f'Create scripts/tests/test-{script_file.stem}{ext} with test cases',
+            })
+
+        all_findings.extend(findings)
+
+    # Check if tests/ directory exists at all
+    if script_files and not tests_dir.exists():
+        all_findings.append({
+            'file': 'scripts/tests/',
+            'line': 0,
+            'severity': 'high',
+            'category': 'tests',
+            'title': 'scripts/tests/ directory does not exist — no unit tests',
+            'detail': '',
+            'action': 'Create scripts/tests/ with test files for each script',
+        })
+
+    # Merge lint findings into all findings
+    all_findings.extend(lint_findings)
+
+    # Build summary
+    by_severity = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0}
+    by_category: dict[str, int] = {}
+    for f in all_findings:
+        sev = f['severity']
+        if sev in by_severity:
+            by_severity[sev] += 1
+        cat = f['category']
+        by_category[cat] = by_category.get(cat, 0) + 1
+
+    total_scripts = sum(len(v) for v in script_inventory.values())
+    status = 'pass'
+    if by_severity['critical'] > 0:
+        status = 'fail'
+    elif by_severity['high'] > 0:
+        status = 'warning'
+    elif total_scripts == 0:
+        status = 'pass'
+
+    lint_issue_count = sum(1 for f in lint_findings if f['category'] == 'lint')
+
+    return {
+        'scanner': 'scripts',
+        'script': 'scan-scripts.py',
+        'version': '2.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': status,
+        'findings': all_findings,
+        'assessments': {
+            'lint_summary': {
+                'tools_used': sorted(lint_tools_used),
+                'files_linted': total_scripts,
+                'lint_issues': lint_issue_count,
+            },
+            'script_summary': {
+                'total_scripts': total_scripts,
+                'by_type': {k: len(v) for k, v in script_inventory.items()},
+                'scripts': {k: v for k, v in script_inventory.items() if v},
+                'missing_tests': missing_tests,
+            },
+        },
+        'summary': {
+            'total_findings': len(all_findings),
+            'by_severity': by_severity,
+            'by_category': by_category,
+            'assessment': '',
+        },
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Scan BMad skill scripts for quality, portability, agentic design, and lint issues',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_skill_scripts(args.skill_path)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0 if result['status'] == 'pass' else 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())