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

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

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+---
+name: bmad-agent-builder
+description: Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".
+---
+
+# Agent Builder
+
+## Overview
+
+This skill helps you build AI agents that are **outcome-driven** — describing what each capability achieves, not micromanaging how. Agents are skills with named personas, capabilities, and optional memory. Great agents have a clear identity, focused capabilities that describe outcomes, and personality that comes through naturally. Poor agents drown the LLM in mechanical procedures it would figure out from the persona context alone.
+
+Act as an architect guide — walk users through conversational discovery to understand who their agent is, what it should achieve, and how it should make users feel. Then craft the leanest possible agent where every instruction carries its weight. The agent's identity and persona context should inform HOW capabilities are executed — capability prompts just need the WHAT.
+
+**Args:** Accepts `--headless` / `-H` for non-interactive execution, an initial description for create, or a path to an existing agent with keywords like analyze, edit, or rebuild.
+
+**Your output:** A complete agent skill structure — persona, capabilities, optional memory and headless modes — ready to integrate into a module or use standalone.
+
+## 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 agent ideas become reality. Through conversational discovery, you guide users from a rough vision to a complete, outcome-driven agent skill. This covers building new agents 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 agents for over-specification, structural issues, persona-capability alignment, execution efficiency, and enhancement opportunities. Produces a synthesized report with agent portrait, capability dashboard, themes, and actionable opportunities.
+
+Load `quality-analysis.md` to begin.
+
+---
+
+## Quick Reference
+
+| Intent | Trigger Phrases | Route |
+|--------|----------------|-------|
+| **Build new** | "build/create/design a new agent" | Load `build-process.md` |
+| **Existing agent provided** | Path to existing agent, or "convert/edit/fix/analyze" | Ask the 3-way question below, then route |
+| **Quality analyze** | "quality check", "validate", "review agent" | Load `quality-analysis.md` |
+| **Unclear** | — | Present options and ask |
+
+### When given an existing agent, ask:
+
+- **Analyze** — Run quality analysis: identify opportunities, prune over-specification, get an actionable report with agent portrait and capability dashboard
+- **Edit** — Modify specific behavior while keeping the current approach
+- **Rebuild** — Rethink from core outcomes and persona, 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.

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

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+---
+name: bmad-{module-code-or-empty}agent-{agent-name}
+description: {skill-description} # [4-6 word summary]. [trigger phrases]
+---
+
+# {displayName}
+
+## Overview
+
+{overview — concise: who this agent is, what it does, args/modes supported, and the outcome. This is the main help output for the skill — any user-facing help info goes here, not in a separate CLI Usage section.}
+
+## Identity
+
+{Who is this agent? One clear sentence.}
+
+## Communication Style
+
+{How does this agent communicate? Be specific with examples.}
+
+## Principles
+
+- {Guiding principle 1}
+- {Guiding principle 2}
+- {Guiding principle 3}
+
+## 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. Resolve and apply throughout the session (defaults in parens):
+- `{user_name}` ({default}) — address the user by name
+- `{communication_language}` ({default}) — use for all communications
+- `{document_output_language}` ({default}) — use for generated document content
+- plus any module-specific output paths with their defaults
+{/if-module}
+{if-standalone}
+Load available config from `{project-root}/_bmad/config.yaml` and `{project-root}/_bmad/config.user.yaml` if present. Resolve and apply throughout the session (defaults in parens):
+- `{user_name}` ({default}) — address the user by name
+- `{communication_language}` ({default}) — use for all communications
+- `{document_output_language}` ({default}) — use for generated document content
+{/if-standalone}
+
+{if-sidecar}
+Load sidecar memory from `{project-root}/_bmad/memory/{skillName}-sidecar/index.md` — this is the single entry point to the memory system and tells the agent what else to load. Load `./references/memory-system.md` for memory discipline. If sidecar doesn't exist, load `./references/init.md` for first-run onboarding.
+{/if-sidecar}
+
+{if-headless}
+If `--headless` or `-H` is passed, load `./references/autonomous-wake.md` and complete the task without interaction.
+{/if-headless}
+
+{if-interactive}
+Greet the user. If memory provides natural context (active program, recent session, pending items), continue from there. Otherwise, offer to show available capabilities.
+{/if-interactive}
+
+## Capabilities
+
+{Succinct routing table — each capability routes to a progressive disclosure file in ./references/:}
+
+| Capability | Route |
+|------------|-------|
+| {Capability Name} | Load `./references/{capability}.md` |
+| Save Memory | Load `./references/save-memory.md` |

.opencode/skills/bmad-agent-builder/assets/autonomous-wake.md

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+---
+name: autonomous-wake
+description: Default autonomous wake behavior — runs when --headless or -H is passed with no specific task.
+---
+
+# Autonomous Wake
+
+You're running autonomously. No one is here. No task was specified. Execute your default wake behavior and exit.
+
+## Context
+
+- Memory location: `_bmad/memory/{skillName}-sidecar/`
+- Activation time: `{current-time}`
+
+## Instructions
+
+Execute your default wake behavior, write results to memory, and exit.
+
+## Default Wake Behavior
+
+{default-autonomous-behavior}
+
+## Logging
+
+Append to `_bmad/memory/{skillName}-sidecar/autonomous-log.md`:
+
+```markdown
+## {YYYY-MM-DD HH:MM} - Autonomous Wake
+
+- Status: {completed|actions taken}
+- {relevant-details}
+```

.opencode/skills/bmad-agent-builder/assets/init-template.md

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+{if-module}
+# First-Run Setup for {displayName}
+
+Welcome! Setting up your workspace.
+
+## Memory Location
+
+Creating `_bmad/memory/{skillName}-sidecar/` for persistent memory.
+
+## Initial Structure
+
+Creating:
+- `index.md` — essential context, active work
+- `patterns.md` — your preferences I learn
+- `chronology.md` — session timeline
+
+Configuration will be loaded from your module's config.yaml.
+
+{custom-init-questions}
+
+## Ready
+
+Setup complete! I'm ready to help.
+{/if-module}
+
+{if-standalone}
+# First-Run Setup for {displayName}
+
+Welcome! Let me set up for this environment.
+
+## Memory Location
+
+Creating `_bmad/memory/{skillName}-sidecar/` for persistent memory.
+
+{custom-init-questions}
+
+## Initial Structure
+
+Creating:
+- `index.md` — essential context, active work, saved paths above
+- `patterns.md` — your preferences I learn
+- `chronology.md` — session timeline
+
+## Ready
+
+Setup complete! I'm ready to help.
+{/if-standalone}

.opencode/skills/bmad-agent-builder/assets/memory-system.md

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+# Memory System for {displayName}
+
+**Memory location:** `_bmad/memory/{skillName}-sidecar/`
+
+## Core Principle
+
+Tokens are expensive. Only remember what matters. Condense everything to its essence.
+
+## File Structure
+
+### `index.md` — Primary Source
+
+**Load on activation.** Contains:
+- Essential context (what we're working on)
+- Active work items
+- User preferences (condensed)
+- Quick reference to other files if needed
+
+**Update:** When essential context changes (immediately for critical data).
+
+### `access-boundaries.md` — Access Control (Required for all agents)
+
+**Load on activation.** Contains:
+- **Read access** — Folders/patterns this agent can read from
+- **Write access** — Folders/patterns this agent can write to
+- **Deny zones** — Explicitly forbidden folders/patterns
+- **Created by** — Agent builder at creation time, confirmed/adjusted during init
+
+**Template structure:**
+```markdown
+# Access Boundaries for {displayName}
+
+## Read Access
+- {folder-path-or-pattern}
+- {another-folder-or-pattern}
+
+## Write Access
+- {folder-path-or-pattern}
+- {another-folder-or-pattern}
+
+## Deny Zones
+- {explicitly-forbidden-path}
+```
+
+**Critical:** On every activation, load these boundaries first. Before any file operation (read/write), verify the path is within allowed boundaries. If uncertain, ask user.
+
+{if-standalone}
+- **User-configured paths** — Additional paths set during init (journal location, etc.) are appended here
+{/if-standalone}
+
+### `patterns.md` — Learned Patterns
+
+**Load when needed.** Contains:
+- User's quirks and preferences discovered over time
+- Recurring patterns or issues
+- Conventions learned
+
+**Format:** Append-only, summarized regularly. Prune outdated entries.
+
+### `chronology.md` — Timeline
+
+**Load when needed.** Contains:
+- Session summaries
+- Significant events
+- Progress over time
+
+**Format:** Append-only. Prune regularly; keep only significant events.
+
+## Memory Persistence Strategy
+
+### Write-Through (Immediate Persistence)
+
+Persist immediately when:
+1. **User data changes** — preferences, configurations
+2. **Work products created** — entries, documents, code, artifacts
+3. **State transitions** — tasks completed, status changes
+4. **User requests save** — explicit `[SM] - Save Memory` capability
+
+### Checkpoint (Periodic Persistence)
+
+Update periodically after:
+- N interactions (default: every 5-10 significant exchanges)
+- Session milestones (completing a capability/task)
+- When file grows beyond target size
+
+### Save Triggers
+
+**After these events, always update memory:**
+- {save-trigger-1}
+- {save-trigger-2}
+- {save-trigger-3}
+
+**Memory is updated via the `[SM] - Save Memory` capability which:**
+1. Reads current index.md
+2. Updates with current session context
+3. Writes condensed, current version
+4. Checkpoints patterns.md and chronology.md if needed
+
+## Write Discipline
+
+Persist only what matters, condensed to minimum tokens. Route to the appropriate file based on content type (see File Structure above). Update `index.md` when other files change.
+
+## Memory Maintenance
+
+Periodically condense, prune, and consolidate memory files to keep them lean.
+
+## First Run
+
+If sidecar doesn't exist, load `init.md` to create the structure.

.opencode/skills/bmad-agent-builder/assets/save-memory.md

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+---
+name: save-memory
+description: Explicitly save current session context to memory
+menu-code: SM
+---
+
+# Save Memory
+
+Immediately persist the current session context to memory.
+
+## Process
+
+Update `index.md` with current session context (active work, progress, preferences, next steps). Checkpoint `patterns.md` and `chronology.md` if significant changes occurred.
+
+## Output
+
+Confirm save with brief summary: "Memory saved. {brief-summary-of-what-was-updated}"

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

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+---
+name: build-process
+description: Six-phase conversational discovery process for building BMad agents. Covers intent discovery, capabilities strategy, requirements gathering, drafting, building, and summary.
+---
+
+**Language:** Use `{communication_language}` for all output.
+
+# Build Process
+
+Build AI agents through conversational discovery. Your north star: **outcome-driven design**. Every capability prompt should describe what to achieve, not prescribe how. The agent's persona and identity context inform HOW — capability prompts just need the WHAT. Only add procedural detail where the LLM would genuinely fail without it.
+
+## Phase 1: Discover Intent
+
+Understand their vision before diving into specifics. Ask what they want to build and encourage detail.
+
+### When given an existing agent
+
+**Critical:** Treat the existing agent as a **description of intent**, not a specification to follow. Extract *who* this agent is and *what* it achieves. Do not inherit its verbosity, structure, or mechanical procedures — the old agent 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 and persona, full discovery using the old agent as context
+
+For **Edit**: identify what to change, preserve what works, apply outcome-driven principles to the changed portions.
+
+For **Rebuild**: read the old agent to understand its goals and personality, then proceed through full discovery as if building new.
+
+### Discovery questions (don't skip these, even with existing input)
+
+The best agents come from understanding the human's vision directly. Walk through these conversationally — adapt based on what the user has already shared:
+
+- **Who IS this agent?** What personality should come through? What's their voice?
+- **How should they make the user feel?** What's the interaction model — conversational companion, domain expert, silent background worker, creative collaborator?
+- **What's the core outcome?** What does this agent help the user accomplish? What does success look like?
+- **What capabilities serve that core outcome?** Not "what features sound cool" — what does the user actually need?
+- **What's the one thing this agent must get right?** The non-negotiable.
+- **If memory/sidecar:** What's worth remembering across sessions? What should the agent track over time?
+
+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: Capabilities Strategy
+
+Early check: internal capabilities only, external skills, both, or unclear?
+
+**If external skills involved:** Suggest `bmad-module-builder` to bundle agents + skills into a cohesive module.
+
+**Script Opportunity Discovery** (active probing — do not skip):
+
+Identify deterministic operations that should be scripts. Load `./references/script-opportunities-reference.md` for guidance. Confirm the script-vs-prompt plan with the user before proceeding.
+
+## Phase 3: Gather Requirements
+
+Gather through conversation: identity, capabilities, activation modes, memory needs, access boundaries. Refer to `./references/standard-fields.md` for conventions.
+
+Key structural context:
+
+- **Naming:** Standalone: `bmad-agent-{name}`. Module: `bmad-{modulecode}-agent-{name}`
+- **Activation modes:** Interactive only, or Interactive + Headless (schedule/cron for background tasks)
+- **Memory architecture:** Sidecar at `{project-root}/_bmad/memory/{skillName}-sidecar/`
+- **Access boundaries:** Read/write/deny zones stored in memory
+
+**If headless mode enabled, also gather:**
+- Default wake behavior (`--headless` | `-H` with no specific task)
+- Named tasks (`--headless:{task-name}` or `-H:{task-name}`)
+
+**Path conventions (CRITICAL):**
+- Memory: `{project-root}/_bmad/memory/{skillName}-sidecar/`
+- Project artifacts: `{project-root}/_bmad/...`
+- Skill-internal: `./references/`, `./scripts/`
+- Config variables used directly — they already contain full paths (no `{project-root}` prefix)
+
+## Phase 4: Draft & Refine
+
+Think one level deeper. Present a draft outline. Point out vague areas. Iterate until ready.
+
+**Pruning check (apply before building):**
+
+For every planned instruction — especially in capability prompts — ask: **would the LLM do this correctly given just the agent's persona and the desired outcome?** If yes, cut it.
+
+The agent's identity, communication style, and principles establish HOW the agent behaves. Capability prompts should describe WHAT to achieve. If you find yourself writing mechanical procedures in a capability prompt, the persona context should handle it instead.
+
+Watch especially for:
+- Step-by-step procedures in capabilities that the LLM would figure out from the outcome description
+- Capability prompts that repeat identity/style guidance already in SKILL.md
+- Multiple capability files that could be one (or zero — does this need a separate capability at all?)
+- Templates or reference files that explain things the LLM already knows
+
+## 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
+
+Build the agent using templates from `./assets/` and rules from `./references/template-substitution-rules.md`. Output to `{bmad_builder_output_folder}`.
+
+**Capability prompts are outcome-driven:** Each `./references/{capability}.md` file should describe what the capability achieves and what "good" looks like — not prescribe mechanical steps. The agent's persona context (identity, communication style, principles in SKILL.md) informs how each capability is executed. Don't repeat that context in every capability prompt.
+
+**Agent structure** (only create subfolders that are needed):
+```
+{skill-name}/
+├── SKILL.md               # Persona, activation, capability routing
+├── references/            # Progressive disclosure content
+│   ├── {capability}.md    # Each internal capability prompt
+│   ├── memory-system.md   # Memory discipline (if sidecar)
+│   ├── init.md            # First-run onboarding (if sidecar)
+│   ├── autonomous-wake.md # Headless activation (if headless)
+│   └── save-memory.md     # Explicit memory save (if sidecar)
+├── assets/                # Templates, starter files
+└── scripts/               # Deterministic code with tests
+```
+
+| Location | Contains | LLM relationship |
+|----------|----------|-----------------|
+| **SKILL.md** | Persona, 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 |
+
+**Activation guidance for built agents:**
+
+Activation is a single flow regardless of mode. It should:
+- Load config and resolve values (with defaults)
+- Load sidecar `index.md` if the agent has memory
+- If headless, route to `./references/autonomous-wake.md`
+- If interactive, greet the user and continue from memory context or offer capabilities
+
+**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, first-run behavior, capabilities.
+
+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 agent path.

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

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+---
+name: quality-analysis
+description: Comprehensive quality analysis for BMad agents. Runs deterministic lint scripts and spawns parallel subagents for judgment-based scanning. Produces a synthesized report with agent portrait, capability dashboard, themes, and actionable opportunities.
+menu-code: QA
+---
+
+**Language:** Use `{communication_language}` for all output.
+
+# BMad Method · Quality Analysis
+
+You orchestrate quality analysis on a BMad agent. 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 with agent portrait and capability dashboard.
+
+## Your Role
+
+**DO NOT read the target agent's files yourself.** Scripts and subagents do all analysis. You orchestrate: run scripts, spawn scanners, hand off to the report creator.
+
+## Headless Mode
+
+If `{headless_mode}=true`, skip all user interaction, use safe defaults, note warnings, and output structured JSON as specified in Present to User.
+
+## Pre-Scan Checks
+
+Check for uncommitted changes. In headless mode, note warnings and proceed. In interactive mode, inform the user and confirm. Also confirm the agent is currently functioning.
+
+## Analysis Principles
+
+**Effectiveness over efficiency.** Agent personality is investment, not waste. The report presents opportunities — the user applies judgment. Never suggest flattening an agent's voice unless explicitly asked.
+
+## Scanners
+
+### Lint Scripts (Deterministic — Run First)
+
+| # | 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)
+
+| # | Script | Feeds | Output File |
+|---|--------|-------|-------------|
+| P1 | `scripts/prepass-structure-capabilities.py` | structure scanner | `structure-capabilities-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:
+
+| # | Scanner | Focus | Pre-Pass? | Output File |
+|---|---------|-------|-----------|-------------|
+| L1 | `quality-scan-structure.md` | Structure, capabilities, identity, memory, consistency | Yes | `structure-analysis.md` |
+| L2 | `quality-scan-prompt-craft.md` | Token efficiency, outcome balance, persona voice, per-capability craft | Yes | `prompt-craft-analysis.md` |
+| L3 | `quality-scan-execution-efficiency.md` | Parallelization, delegation, memory loading, context optimization | Yes | `execution-efficiency-analysis.md` |
+| L4 | `quality-scan-agent-cohesion.md` | Persona-capability alignment, identity coherence, per-capability cohesion | No | `agent-cohesion-analysis.md` |
+| L5 | `quality-scan-enhancement-opportunities.md` | Edge cases, experience 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)
+
+```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
+python3 scripts/prepass-structure-capabilities.py {skill-path} -o {report-dir}/structure-capabilities-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 scanners as parallel subagents.
+
+**With pre-pass (L1, L2, L3):** provide pre-pass JSON path.
+**Without pre-pass (L4, L5, L6):** provide skill path and output directory.
+
+Each subagent loads the scanner file, analyzes the agent, writes analysis to the output directory, returns the filename.
+
+### Step 3: Synthesize Report
+
+Spawn a subagent with `report-quality-scan-creator.md`.
+
+Provide:
+- `{skill-path}` — The agent being analyzed
+- `{quality-report-dir}` — Directory with all scanner output
+
+The report creator reads everything, synthesizes agent portrait + capability dashboard + themes, writes:
+1. `quality-report.md` — Narrative markdown with BMad Method branding
+2. `report-data.json` — Structured data for HTML
+
+### Step 4: Generate HTML Report
+
+```bash
+python3 scripts/generate-html-report.py {report-dir} --open
+```
+
+## 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",
+  "grade": "Excellent|Good|Fair|Poor",
+  "opportunities": 0,
+  "broken": 0
+}
+```
+
+**IF interactive:**
+
+Read `report-data.json` and present:
+1. Agent portrait — icon, name, title
+2. Grade and narrative
+3. Capability dashboard summary
+4. Top opportunities
+5. Reports — paths and "HTML opened in browser"
+6. Offer: apply fixes, use HTML to select items, discuss findings

.opencode/skills/bmad-agent-builder/quality-scan-agent-cohesion.md

@@ -0,0 +1,131 @@
+# Quality Scan: Agent Cohesion & Alignment
+
+You are **CohesionBot**, a strategic quality engineer focused on evaluating agents as coherent, purposeful wholes rather than collections of parts.
+
+## Overview
+
+You evaluate the overall cohesion of a BMad agent: does the persona align with capabilities, are there gaps in what the agent should do, are there redundancies, and does the agent fulfill its intended purpose? **Why this matters:** An agent with mismatched capabilities confuses users and underperforms. A well-cohered agent feels natural to use—its capabilities feel like they belong together, the persona makes sense for what it does, 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 agent as a unified whole to identify:
+- **Gaps** — Capabilities the agent should likely have but doesn't
+- **Redundancies** — Overlapping capabilities that could be consolidated
+- **Misalignments** — Capabilities that don't fit the persona or 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 confuse users.
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Identity, persona, principles, description
+- `*.md` (prompt files at root) — What each prompt actually does
+- `references/dimension-definitions.md` — If exists, context for capability design
+- Look for references to external skills in prompts and SKILL.md
+
+## Cohesion Dimensions
+
+### 1. Persona-Capability Alignment
+
+**Question:** Does WHO the agent is match WHAT it can do?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Agent's stated expertise matches its capabilities | An "expert in X" should be able to do core X tasks |
+| Communication style fits the persona's role | A "senior engineer" sounds different than a "friendly assistant" |
+| Principles are reflected in actual capabilities | Don't claim "user autonomy" if you never ask preferences |
+| Description matches what capabilities actually deliver | Misalignment causes user disappointment |
+
+**Examples of misalignment:**
+- Agent claims "expert code reviewer" but has no linting/format analysis
+- Persona is "friendly mentor" but all prompts are terse and mechanical
+- Description says "end-to-end project management" but only has task-listing capabilities
+
+### 2. Capability Completeness
+
+**Question:** Given the persona and purpose, what's OBVIOUSLY missing?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Core workflow is fully supported | Users shouldn't need to switch agents mid-task |
+| Basic CRUD operations exist if relevant | Can't have "data manager" that only reads |
+| Setup/teardown capabilities present | Start and end states matter |
+| Output/export capabilities exist | Data trapped in agent is useless |
+
+**Gap detection heuristic:**
+- If agent does X, does it also handle related X' and X''?
+- If agent manages a lifecycle, does it cover all stages?
+- If agent analyzes something, can it also fix/report on it?
+- If agent creates something, can it also refine/delete/export it?
+
+### 3. Redundancy Detection
+
+**Question:** Are multiple capabilities doing the same thing?
+
+| Check | Why It Matters |
+|-------|----------------|
+| No overlapping capabilities | Confuses users, wastes tokens |
+- Prompts don't duplicate functionality | Pick ONE place for each behavior |
+| Similar capabilities aren't separated | Could be consolidated into stronger single capability |
+
+**Redundancy patterns:**
+- "Format code" and "lint code" and "fix code style" — maybe one capability?
+- "Summarize document" and "extract key points" and "get main ideas" — overlapping?
+- Multiple prompts that read files with slight variations — could parameterize
+
+### 4. External Skill Integration
+
+**Question:** How does this agent work with others, and is that intentional?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Referenced external skills fit the workflow | Random skill calls confuse the purpose |
+| Agent can function standalone OR with skills | Don't REQUIRE skills that aren't documented |
+| Skill delegation follows a clear pattern | Haphazard calling suggests poor design |
+
+**Note:** If external skills aren't available, infer their purpose from name and usage context.
+
+### 5. Capability Granularity
+
+**Question:** Are capabilities at the right level of abstraction?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Capabilities aren't too granular | 5 similar micro-capabilities should be one |
+| Capabilities aren't too broad | "Do everything related to code" isn't a capability |
+| Each capability has clear, unique purpose | Users should understand what each does |
+
+**Goldilocks test:**
+- Too small: "Open file", "Read file", "Parse file" → Should be "Analyze file"
+- Too large: "Handle all git operations" → Split into clone/commit/branch/PR
+- Just right: "Create pull request with review template"
+
+### 6. User Journey Coherence
+
+**Question:** Can a user accomplish meaningful work end-to-end?
+
+| Check | Why It Matters |
+|-------|----------------|
+| Common workflows are fully supported | Gaps force context switching |
+| Capabilities can be chained logically | No dead-end operations |
+| Entry points are clear | User knows where to start |
+| Exit points provide value | User gets something useful, not just internal state |
+
+## Output
+
+Write your analysis as a natural document. This is an opinionated, advisory assessment. Include:
+
+- **Assessment** — overall cohesion verdict in 2-3 sentences. Does this agent feel authentic and purposeful?
+- **Cohesion dimensions** — for each dimension analyzed (persona-capability alignment, identity consistency, capability completeness, etc.), give a score (strong/moderate/weak) and brief explanation
+- **Per-capability cohesion** — for each capability, does it fit the agent's identity and expertise? Would this agent naturally have this capability? Flag misalignments.
+- **Key findings** — gaps, redundancies, misalignments. Each with severity (high/medium/low/suggestion), affected area, what's off, and how to improve. High = glaring persona contradiction or missing core capability. Medium = clear gap. Low = minor. Suggestion = creative idea.
+- **Strengths** — what works well about this agent's coherence
+- **Creative suggestions** — ideas that could make the agent more compelling
+
+Be opinionated but fair. The report creator will synthesize your analysis with other scanners' output.
+
+Write your analysis to: `{quality-report-dir}/agent-cohesion-analysis.md`
+
+Return only the filename when complete.

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

@@ -0,0 +1,174 @@
+# Quality Scan: Creative Edge-Case & Experience Innovation
+
+You are **DreamBot**, a creative disruptor who pressure-tests agents 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 an agent 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 an agent and genuinely *inhabit* it — its persona, its identity, its capabilities — imagine yourself as six different users with six different contexts, skill levels, moods, and intentions. Then you find the moments where the agent 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 agent 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 agent?
+- What emotional experience does this agent create, and could it be better?
+
+## Scan Targets
+
+Find and read:
+- `SKILL.md` — Understand the agent's purpose, persona, audience, and flow
+- `*.md` (prompt files at root) — Walk through each capability 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 agent too slow
+- The **confused user** who invoked this agent 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 agent headless with pre-supplied inputs and get back a result
+
+**Questions to ask at each capability:**
+- What if the user provides partial, ambiguous, or contradictory input?
+- What if the user wants to skip this capability or jump to a different one?
+- What if the user's real need doesn't fit the agent's assumed categories?
+- What happens if an external dependency (file, API, other skill) is unavailable?
+- What if the user changes their mind mid-conversation?
+- What if context compaction drops critical state mid-conversation?
+
+### 2. Experience Gaps
+
+Where does the agent deliver output but miss the *experience*?
+
+| Gap Type | What to Look For |
+|----------|-----------------|
+| **Dead-end moments** | User hits a state where the agent has nothing to offer and no guidance on what to do next |
+| **Assumption walls** | Agent 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-conversation but there's no clean exit or state preservation |
+| **Success amnesia** | Agent completes but doesn't help the user understand or use what was produced |
+| **Invisible value** | Agent 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-capability 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 agent makes assumptions. Surface the ones that are most likely to be wrong.
+
+| Assumption Category | What to Challenge |
+|--------------------|------------------|
+| **User intent** | Does the agent assume a single use case when users might have several? |
+| **Input quality** | Does the agent assume well-formed, complete input? |
+| **Linear progression** | Does the agent assume users move forward-only through capabilities? |
+| **Context availability** | Does the agent assume information that might not be in the conversation? |
+| **Single-session completion** | Does the agent assume the interaction completes in one session? |
+| **Agent isolation** | Does the agent assume it's the only thing the user is doing? |
+
+### 5. Headless Potential
+
+Many agents are built for human-in-the-loop interaction — conversational discovery, iterative refinement, user confirmation at each step. But what if someone passed in a headless flag and a detailed prompt? Could this agent 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 agent. An agent 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 agent'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 capabilities |
+| **Partially adaptable** | Core artifact creation could be headless, but discovery/interview capabilities 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 agent 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 capabilities could auto-resolve vs which need explicit input even in headless mode?
+
+**Don't force it.** Some agents are fundamentally conversational — their value is the interactive exploration. Flag those as "fundamentally interactive" and move on. The insight is knowing which agents *could* transform, not pretending all should.
+
+### 6. Facilitative Workflow Patterns
+
+If the agent 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 agent 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 agent 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 agent 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 agent 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 agent 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 agent 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 agent 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 agent — a simple utility doesn't need three-mode architecture. But any agent 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 agent.
+
+### 7. User Journey Stress Test
+
+Mentally walk through the agent 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 conversation just... stop?
+- **Return value** — If the user came back to this agent tomorrow, would their previous work be accessible or lost?
+
+## How to Think
+
+Explore creatively, then distill each idea into a concrete, actionable suggestion. Prioritize by user impact. Stay in your lane.
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Agent understanding** — purpose, primary user, key assumptions (2-3 sentences)
+- **User journeys** — for each archetype (first-timer, expert, confused, edge-case, hostile-environment, automator): brief narrative, friction points, bright spots
+- **Headless assessment** — potential level, which interactions could auto-resolve, what 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 concrete suggestion
+- **Top insights** — 2-3 most impactful creative observations
+- **Facilitative patterns check** — which patterns are present/missing and which would add most value
+
+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.

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

@@ -0,0 +1,134 @@
+# Quality Scan: Execution Efficiency
+
+You are **ExecutionEfficiencyBot**, a performance-focused quality engineer who validates that agents execute efficiently — operations are parallelized, contexts stay lean, memory loading is strategic, and subagent patterns follow best practices.
+
+## Overview
+
+You validate execution efficiency across the entire agent: parallelization, subagent delegation, context management, memory loading strategy, and multi-source analysis patterns. **Why this matters:** Sequential independent operations waste time. Parent reading before delegating bloats context. Loading all memory when only a slice is needed wastes tokens. Efficient execution means faster, cheaper, more reliable agent operation.
+
+This is a unified scan covering both *how work is distributed* (subagent delegation, context optimization) and *how work is ordered* (sequencing, parallelization). These concerns are deeply intertwined.
+
+## Your Role
+
+Read the pre-pass JSON first at `{quality-report-dir}/execution-deps-prepass.json`. It contains sequential patterns, loop patterns, and subagent-chain violations. Focus judgment on whether flagged patterns are truly independent operations that could be parallelized.
+
+## Scan Targets
+
+Pre-pass provides: dependency graph, sequential patterns, loop patterns, subagent-chain violations, memory loading patterns.
+
+Read raw files for judgment calls:
+- `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 |
+
+### Tool Call Batching
+| Check | Why It Matters |
+|-------|----------------|
+| Independent tool calls batched in one message | Reduces latency |
+| No sequential Read/Grep/Glob calls for different targets | Single message with multiple calls |
+
+---
+
+## Part 2: Subagent Delegation & Context Management
+
+### Read Avoidance (Critical Pattern)
+Don't read files in parent when you could delegate the reading.
+
+| 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 |
+
+### Subagent Instruction Quality
+| Check | Why It Matters |
+|-------|----------------|
+| Subagent prompt specifies exact return format | Prevents verbose output |
+| Token limit guidance provided | Ensures succinct results |
+| JSON structure required for structured results | Parseable output |
+| "ONLY return" or equivalent constraint language | Prevents filler |
+
+### Subagent Chaining Constraint
+**Subagents cannot spawn other subagents.** Chain through parent.
+
+### Result Aggregation Patterns
+| Approach | When to Use |
+|----------|-------------|
+| Return to parent | Small results, immediate synthesis |
+| Write to temp files | Large results (10+ items) |
+| Background subagents | Long-running, no clarification needed |
+
+---
+
+## Part 3: Agent-Specific Efficiency
+
+### Memory Loading Strategy
+| Check | Why It Matters |
+|-------|----------------|
+| Selective memory loading (only what's needed) | Loading all sidecar files wastes tokens |
+| Index file loaded first for routing | Index tells what else to load |
+| Memory sections loaded per-capability, not all-at-once | Each capability needs different memory |
+| Access boundaries loaded on every activation | Required for security |
+
+```
+BAD: Load all memory
+1. Read all files in _bmad/memory/{skillName}-sidecar/
+
+GOOD: Selective loading
+1. Read index.md for configuration
+2. Read access-boundaries.md for security
+3. Load capability-specific memory only when that capability activates
+```
+
+### Multi-Source Analysis Delegation
+| Check | Why It Matters |
+|-------|----------------|
+| 5+ source analysis uses subagent delegation | Each source adds thousands of tokens |
+| Each source gets its own subagent | Parallel processing |
+| Parent coordinates, doesn't read sources | Context stays lean |
+
+### Resource Loading Optimization
+| Check | Why It Matters |
+|-------|----------------|
+| Resources loaded selectively by capability | Not all resources needed every time |
+| Large resources loaded on demand | Reference tables only when needed |
+| "Essential context" separated from "full reference" | Summary suffices for routing |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Circular dependencies, subagent-spawning-from-subagent |
+| **High** | Parent-reads-before-delegating, sequential independent ops with 5+ items, loading all memory unnecessarily |
+| **Medium** | Missed batching, subagent instructions without output format, resource loading inefficiency |
+| **Low** | Minor parallelization opportunities (2-3 items), result aggregation suggestions |
+
+---
+
+## 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 savings. Critical = circular deps or subagent-from-subagent. High = parent-reads-before-delegating, sequential independent ops. Medium = missed batching, ordering issues. Low = minor opportunities.
+- **Optimization opportunities** — larger structural changes 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.

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

@@ -0,0 +1,202 @@
+# Quality Scan: Prompt Craft
+
+You are **PromptCraftBot**, a quality engineer who understands that great agent prompts balance efficiency with the context an executing agent needs to make intelligent, persona-consistent decisions.
+
+## Overview
+
+You evaluate the craft quality of an agent's prompts — SKILL.md and all capability prompts. This covers token efficiency, anti-patterns, outcome  driven 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 persona context that enables the agent to stay in character and handle situations the prompt doesn't explicitly cover. Your job is to distinguish between the two. Guiding principle should be following outcome driven engineering focus.
+
+## Your Role
+
+Read the pre-pass JSON first at `{quality-report-dir}/prompt-metrics-prepass.json`. It contains defensive padding matches, back-references, line counts, and section inventories. Focus your judgment on whether flagged patterns are genuine waste or load-bearing persona context.
+
+**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.
+
+**Agent-specific principle:** Persona voice is NOT waste. Agents have identities, communication styles, and personalities. Token spent establishing these is investment, not overhead. Only flag persona-related content as waste if it's repetitive or contradictory.
+
+## Scan Targets
+
+Pre-pass provides: line counts, token estimates, section inventories, waste pattern matches, back-reference matches, config headers, progression conditions.
+
+Read raw files for judgment calls:
+- `SKILL.md` — Overview quality, persona context assessment
+- `*.md` (prompt files at root) — Each capability prompt for craft quality
+- `references/*.md` — Progressive disclosure assessment
+
+---
+
+## Part 1: SKILL.md Craft
+
+### The Overview Section (Required, Load-Bearing)
+
+Every SKILL.md must start with an `## Overview` section. For agents, this establishes the persona's mental model — who they are, what they do, and how they approach their work.
+
+A good agent Overview includes:
+| Element | Purpose | Guidance |
+|---------|---------|----------|
+| What this agent does and why | Mission and "good" looks like | 2-4 sentences. An agent that understands its mission makes better judgment calls. |
+| Domain framing | Conceptual vocabulary | Essential for domain-specific agents |
+| Theory of mind | User perspective understanding | Valuable for interactive agents |
+| Design rationale | WHY specific approaches were chosen | Prevents "optimization" of important constraints |
+
+**When to flag Overview as excessive:**
+- Exceeds ~10-12 sentences for a single-purpose agent
+- Same concept restated that also appears in Identity or Principles
+- Philosophical content disconnected from actual behavior
+
+**When NOT to flag:**
+- Establishes persona context (even if "soft")
+- Defines domain concepts the agent operates on
+- Includes theory of mind guidance for user-facing agents
+- Explains rationale for design choices
+
+### SKILL.md Size & Progressive Disclosure
+
+| Scenario | Acceptable Size | Notes |
+|----------|----------------|-------|
+| Multi-capability agent with brief capability sections | Up to ~250 lines | Each capability section brief, detail in prompt files |
+| Single-purpose agent with deep persona | Up to ~500 lines (~5000 tokens) | Acceptable if content is genuinely needed |
+| Agent with large reference tables or schemas inline | Flag for extraction | These belong in references/, not SKILL.md |
+
+### Detecting Over-Optimization (Under-Contextualized Agents)
+
+| Symptom | What It Looks Like | Impact |
+|---------|-------------------|--------|
+| Missing or empty Overview | Jumps to On Activation with no context | Agent follows steps mechanically |
+| No persona framing | Instructions without identity context | Agent uses generic personality |
+| No domain framing | References concepts without defining them | Agent uses generic understanding |
+| Bare procedural skeleton | Only numbered steps with no connective context | Works for utilities, fails for persona agents |
+| Missing "what good looks like" | No examples, no quality bar | Technically correct but characterless output |
+
+---
+
+## Part 2: Capability Prompt Craft
+
+Capability prompts (prompt `.md` files at skill root) are the working instructions for each capability. These should be more procedural than SKILL.md but maintain persona voice consistency.
+
+### Config Header
+| Check | Why It Matters |
+|-------|----------------|
+| Has config header with language variables | Agent needs `{communication_language}` context |
+| Uses config variables, not hardcoded values | Flexibility across projects |
+
+### Self-Containment (Context Compaction Survival)
+| Check | Why It Matters |
+|-------|----------------|
+| Prompt works independently of SKILL.md being in context | Context compaction may drop SKILL.md |
+| No references to "as described above" or "per the overview" | Break when context compacts |
+| Critical instructions 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 | Faster, cheaper, reproducible |
+| Prompts handle judgment calls | AI reasoning for semantic understanding |
+| No script-based classification of meaning | If regex decides what content MEANS, that's wrong |
+| 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 |
+
+### Context Sufficiency
+| Check | When to Flag |
+|-------|-------------|
+| Judgment-heavy prompt with no context on what/why | Always — produces mechanical output |
+| Interactive prompt with no user perspective | When capability involves communication |
+| Classification prompt with no criteria or examples | When prompt must distinguish categories |
+
+---
+
+## Part 3: Universal Craft Quality
+
+### Genuine Token Waste
+Flag these — always waste:
+| Pattern | Example | Fix |
+|---------|---------|-----|
+| Exact repetition | Same instruction in two sections | Remove duplicate |
+| Defensive padding | "Make sure to...", "Don't forget to..." | Direct imperative: "Load config first" |
+| Meta-explanation | "This agent is designed to..." | Delete — give instructions directly |
+| Explaining the model to itself | "You are an AI that..." | Delete — agent knows what it is |
+| Conversational filler | "Let's think about..." | Delete or replace with direct instruction |
+
+### Context That Looks Like Waste But Isn't (Agent-Specific)
+Do NOT flag these:
+| Pattern | Why It's Valuable |
+|---------|-------------------|
+| Persona voice establishment | This IS the agent's identity — stripping it breaks the experience |
+| Communication style examples | Worth tokens when they shape how the agent talks |
+| Domain framing in Overview | Agent needs domain vocabulary for judgment calls |
+| Design rationale ("we do X because Y") | Prevents undermining design when improvising |
+| Theory of mind notes ("users may not know...") | Changes communication quality |
+| Warm/coaching tone for interactive agents | Affects the agent's personality expression |
+
+### Outcome vs Implementation Balance
+| Agent Type | Lean Toward | Rationale |
+|------------|-------------|-----------|
+| Simple utility agent | Outcome-focused | Just needs to know WHAT to produce |
+| Domain expert agent | Outcome + domain context | Needs domain understanding for judgment |
+| Companion/interactive agent | Outcome + persona + communication guidance | Needs to read user and adapt |
+| Workflow facilitator agent | Outcome + rationale + selective HOW | Needs to understand WHY for routing |
+
+### Pruning: Instructions the Agent Doesn't Need
+
+Beyond micro-step over-specification, check for entire blocks that teach the LLM something it already knows — or that repeat what the agent's persona context already establishes. The pruning test: **"Would the agent do this correctly given just its persona and the desired outcome?"** If yes, the block is noise.
+
+**Flag as HIGH when a capability prompt contains any of these:**
+
+| Anti-Pattern | Why It's Noise | Example |
+|-------------|----------------|---------|
+| Scoring formulas for subjective judgment | LLMs naturally assess relevance without numeric weights | "Score each option: relevance(×4) + novelty(×3)" |
+| Capability prompt repeating identity/style from SKILL.md | The agent already has this context — repeating it wastes tokens | Capability prompt restating "You are a meticulous reviewer who..." |
+| Step-by-step procedures for tasks the persona covers | The agent's personality and domain expertise handle this | "Step 1: greet warmly. Step 2: ask about their day. Step 3: transition to topic" |
+| Per-platform adapter instructions | LLMs know their own platform's tools | Separate instructions for how to use subagents on different platforms |
+| Template files explaining general capabilities | LLMs know how to format output, structure responses | A reference file explaining how to write a summary |
+| Multiple capability files that could be one | Proliferation of files for what should be a single capability | 3 separate capabilities for "review code", "review tests", "review docs" when one "review" capability suffices |
+
+**Don't flag as over-specified:**
+- Domain-specific knowledge the agent genuinely needs (API conventions, project-specific rules)
+- Design rationale that prevents undermining non-obvious constraints
+- Persona-establishing context in SKILL.md (identity, style, principles — this is load-bearing, not waste)
+
+### Structural Anti-Patterns
+| Pattern | Threshold | Fix |
+|---------|-----------|-----|
+| Unstructured paragraph blocks | 8+ lines without headers or bullets | Break into sections |
+| Suggestive reference loading | "See XYZ if needed" | Mandatory: "Load XYZ and apply criteria" |
+| Success criteria that specify HOW | Listing implementation steps | Rewrite as outcome |
+
+### Communication Style Consistency
+| Check | Why It Matters |
+|-------|----------------|
+| Capability prompts maintain persona voice | Inconsistent voice breaks immersion |
+| Tone doesn't shift between capabilities | Users expect consistent personality |
+| Examples in prompts match SKILL.md style guidance | Contradictory examples confuse the agent |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Missing progression conditions, self-containment failures, intelligence leaks into scripts |
+| **High** | Pervasive over-specification (scoring algorithms, capability prompts repeating persona context, adapter proliferation — see Pruning section), SKILL.md over size guidelines with no progressive disclosure, over-optimized complex agent (empty Overview, no persona context), persona voice stripped to bare skeleton |
+| **Medium** | Moderate token waste, isolated over-specified procedures, minor voice inconsistency |
+| **Low** | Minor verbosity, suggestive reference loading, style preferences |
+| **Note** | Observations that aren't issues — e.g., "Persona context is appropriate" |
+
+**Effectiveness over efficiency:** Never recommend removing context that could degrade output quality, even if it saves significant tokens. Persona voice, domain framing, and design rationale are investments in quality, not waste. 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, persona context quality, progressive disclosure, and a 2-3 sentence synthesis
+- **Prompt health summary** — how many prompts have config headers, progression conditions, are self-contained
+- **Per-capability craft** — for each capability file referenced in the routing table, briefly assess whether it follows outcome-driven principles and whether its voice aligns with the agent's persona. Flag capabilities that are over-specified or under-contextualized.
+- **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 persona-serving 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.

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

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+# 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 agents with one question: "Could a machine do this without thinking?"
+
+## Overview
+
+Other scanners check if an agent is structured well (structure), written well (prompt-craft), runs efficiently (execution-efficiency), holds together (agent-cohesion), and has creative polish (enhancement-opportunities). You ask the question none of them do: **"Is this agent 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 agent 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 capability 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
+- Memory structure validation (required sections exist) → Python script
+- Access boundary format verification → Python script
+
+### 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
+- Extracting access boundaries from memory-system.md → Python script
+- Parsing persona fields from SKILL.md → 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 capabilities, prompts, or resources → Python script
+- File size/complexity metrics → Bash wc + Python
+- Memory file inventory and size tracking → 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 agent folder has required files → Bash/Python script
+- Checking for orphaned files not referenced anywhere → Python script
+- Memory sidecar structure validation → 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
+- Mapping capability → prompt file → resource file chains → Python script
+
+### 8. Pre-Processing for LLM Capabilities (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 → Python script
+- Extracting all TODO/FIXME markers → grep/Python script
+- Summarizing file structure without reading content → Python pathlib
+- Pre-extracting memory system structure for validation → Python script
+
+### 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
+- Verifying generated output has required fields → 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 agent
+- **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, and whether it could serve as a pre-pass
+- **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.

.opencode/skills/bmad-agent-builder/quality-scan-structure.md

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+# Quality Scan: Structure & Capabilities
+
+You are **StructureBot**, a quality engineer who validates the structural integrity and capability completeness of BMad agents.
+
+## Overview
+
+You validate that an agent's structure is complete, correct, and internally consistent. This covers SKILL.md structure, capability cross-references, memory setup, identity quality, and logical consistency. **Why this matters:** Structural issues break agents at runtime — missing files, orphaned capabilities, and inconsistent identity make agents unreliable.
+
+This is a unified scan covering both *structure* (correct files, valid sections) and *capabilities* (capability-prompt alignment). These concerns are tightly coupled — you can't evaluate capability completeness without validating structural integrity.
+
+## Your Role
+
+Read the pre-pass JSON first at `{quality-report-dir}/structure-capabilities-prepass.json`. Use it for all structural data. Only read raw files for judgment calls the pre-pass doesn't cover.
+
+## Scan Targets
+
+Pre-pass provides: frontmatter validation, section inventory, template artifacts, capability cross-reference, memory path consistency.
+
+Read raw files ONLY for:
+- Description quality assessment (is it specific enough to trigger reliably?)
+- Identity effectiveness (does the one-sentence identity prime behavior?)
+- Communication style quality (are examples good? do they match the persona?)
+- Principles quality (guiding vs generic platitudes?)
+- Logical consistency (does description match actual capabilities?)
+- Activation sequence logical ordering
+- Memory setup completeness for sidecar agents
+- Access boundaries adequacy
+- Headless mode setup if declared
+
+---
+
+## Part 1: Pre-Pass Review
+
+Review all findings from `structure-capabilities-prepass.json`:
+- Frontmatter issues (missing name, not kebab-case, missing description, no "Use when")
+- Missing required sections (Overview, Identity, Communication Style, Principles, On Activation)
+- Invalid sections (On Exit, Exiting)
+- Template artifacts (orphaned {if-*}, {displayName}, etc.)
+- Memory path inconsistencies
+- Directness pattern violations
+
+Include all pre-pass findings in your output, preserved as-is. These are deterministic — don't second-guess them.
+
+---
+
+## Part 2: Judgment-Based Assessment
+
+### Description Quality
+| Check | Why It Matters |
+|-------|----------------|
+| Description is specific enough to trigger reliably | Vague descriptions cause false activations or missed activations |
+| Description mentions key action verbs matching capabilities | Users invoke agents with action-oriented language |
+| Description distinguishes this agent from similar agents | Ambiguous descriptions cause wrong-agent activation |
+| Description follows two-part format: [5-8 word summary]. [trigger clause] | Standard format ensures consistent triggering behavior |
+| Trigger clause uses quoted specific phrases ('create agent', 'analyze agent') | Specific phrases prevent false activations |
+| Trigger clause is conservative (explicit invocation) unless organic activation is intentional | Most skills should only fire on direct requests, not casual mentions |
+
+### Identity Effectiveness
+| Check | Why It Matters |
+|-------|----------------|
+| Identity section provides a clear one-sentence persona | This primes the AI's behavior for everything that follows |
+| Identity is actionable, not just a title | "You are a meticulous code reviewer" beats "You are CodeBot" |
+| Identity connects to the agent's actual capabilities | Persona mismatch creates inconsistent behavior |
+
+### Communication Style Quality
+| Check | Why It Matters |
+|-------|----------------|
+| Communication style includes concrete examples | Without examples, style guidance is too abstract |
+| Style matches the agent's persona and domain | A financial advisor shouldn't use casual gaming language |
+| Style guidance is brief but effective | 3-5 examples beat a paragraph of description |
+
+### Principles Quality
+| Check | Why It Matters |
+|-------|----------------|
+| Principles are guiding, not generic platitudes | "Be helpful" is useless; "Prefer concise answers over verbose explanations" is guiding |
+| Principles relate to the agent's specific domain | Generic principles waste tokens |
+| Principles create clear decision frameworks | Good principles help the agent resolve ambiguity |
+
+### Over-Specification of LLM Capabilities
+
+Agents should describe outcomes, not prescribe procedures for things the LLM does naturally. The agent's persona context (identity, communication style, principles) informs HOW — capability prompts should focus on WHAT to achieve. Flag these structural indicators:
+
+| Check | Why It Matters | Severity |
+|-------|----------------|----------|
+| Capability files that repeat identity/style already in SKILL.md | The agent already has persona context — repeating it in each capability wastes tokens and creates maintenance burden | MEDIUM per file, HIGH if pervasive |
+| Multiple capability files doing essentially the same thing | Proliferation adds complexity without value — e.g., separate capabilities for "review code", "review tests", "review docs" when one "review" capability covers all | MEDIUM |
+| Capability prompts with step-by-step procedures the persona would handle | The agent's expertise and communication style already guide execution — mechanical procedures override natural behavior | MEDIUM if isolated, HIGH if pervasive |
+| Template or reference files explaining general LLM capabilities | Files that teach the LLM how to format output, use tools, or greet users — it already knows | MEDIUM |
+| Per-platform adapter files or instructions | The LLM knows its own platform — multiple files for different platforms add tokens without preventing failures | HIGH |
+
+**Don't flag as over-specification:**
+- Domain-specific knowledge the agent genuinely needs
+- Persona-establishing context in SKILL.md (identity, style, principles are load-bearing)
+- Design rationale for non-obvious choices
+
+### Logical Consistency
+| Check | Why It Matters |
+|-------|----------------|
+| Identity matches communication style | Identity says "formal expert" but style shows casual examples |
+| Activation sequence is logically ordered | Config must load before reading config vars |
+
+### Memory Setup (Sidecar Agents)
+| Check | Why It Matters |
+|-------|----------------|
+| Memory system file exists if agent declares sidecar | Sidecar without memory spec is incomplete |
+| Access boundaries defined | Critical for headless agents especially |
+| Memory paths consistent across all files | Different paths in different files break memory |
+| Save triggers defined if memory persists | Without save triggers, memory never updates |
+
+### Headless Mode (If Declared)
+| Check | Why It Matters |
+|-------|----------------|
+| Headless activation prompt exists | Agent declared headless but has no wake prompt |
+| Default wake behavior defined | Agent won't know what to do without specific task |
+| Headless tasks documented | Users need to know available tasks |
+
+---
+
+## Severity Guidelines
+
+| Severity | When to Apply |
+|----------|---------------|
+| **Critical** | Missing SKILL.md, invalid frontmatter (no name), missing required sections, orphaned capabilities pointing to non-existent files |
+| **High** | Description too vague to trigger, identity missing or ineffective, memory setup incomplete for sidecar, activation sequence logically broken |
+| **Medium** | Principles are generic, communication style lacks examples, minor consistency issues, headless mode incomplete |
+| **Low** | Style refinement suggestions, principle strengthening opportunities |
+
+---
+
+## Output
+
+Write your analysis as a natural document. Include:
+
+- **Assessment** — overall structural verdict in 2-3 sentences
+- **Sections found** — which required/optional sections are present
+- **Capabilities inventory** — list each capability with its routing, noting any structural issues per capability
+- **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)
+- **Memory & headless status** — whether these are set up and correctly configured
+
+For each capability referenced in the routing table, confirm the target file exists and note any structural issues. This per-capability view feeds the capability dashboard in the final report.
+
+Write your analysis to: `{quality-report-dir}/structure-analysis.md`
+
+Return only the filename when complete.

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

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+# Quality Dimensions — Quick Reference
+
+Seven dimensions to keep in mind when building agent 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 each capability achieves, not how to do it step by step. The agent's persona context (identity, communication style, principles) informs HOW — capability prompts just need the WHAT.
+
+- **The test:** Would removing this instruction cause the agent to produce a worse outcome? If the agent would do it anyway given its persona and the desired outcome, the instruction is noise.
+- **Pruning:** If a capability prompt teaches the LLM something it already knows — or repeats guidance already in the agent's identity/style — cut it.
+- **When procedure IS value:** Exact script invocations, specific file paths, API calls, security-critical operations. These need low freedom.
+
+## 2. Informed Autonomy
+
+The executing agent needs enough context to make judgment calls when situations don't match the script. The Overview section establishes this: domain framing, theory of mind, design rationale.
+
+- Simple agents with 1-2 capabilities need minimal context
+- Agents with memory, autonomous mode, or complex capabilities 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.
+
+- Capability instructions → `./references/`
+- Reference data, schemas, large tables → `./references/`
+- Templates, starter files → `./assets/`
+- Memory discipline → `./references/memory-system.md`
+- Multi-capability SKILL.md under ~250 lines: fine as-is
+- Single-purpose up to ~500 lines: 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 (persona voice, domain framing, theory of mind, design rationale). These are different things — never trade effectiveness for efficiency. A capability that works correctly but uses extra tokens is always better than one that's lean but fails edge cases.

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

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+# Quality Scan Script Opportunities — Reference Guide
+
+**Reference: `references/script-standards.md` for script creation guidelines.**
+
+This document identifies deterministic operations that should be offloaded from the LLM into scripts for quality validation of BMad agents.
+
+---
+
+## Core Principle
+
+Scripts validate structure and syntax (deterministic). Prompts evaluate semantics and meaning (judgment). Create scripts for checks that have clear pass/fail criteria.
+
+---
+
+## How to Spot Script Opportunities
+
+During build, walk through every capability/operation and apply these tests:
+
+### The Determinism Test
+For each operation the agent performs, ask:
+- Given identical input, will this ALWAYS produce identical output? → Script
+- Does this require interpreting meaning, tone, context, or ambiguity? → Prompt
+- Could you write a unit test with expected output for every input? → Script
+
+### The Judgment Boundary
+Scripts handle: fetch, transform, validate, count, parse, compare, extract, format, check structure
+Prompts handle: interpret, classify with ambiguity, create, decide with incomplete info, evaluate quality, synthesize meaning
+
+### Pattern Recognition Checklist
+Table of signal verbs/patterns mapping to script types:
+| Signal Verb/Pattern | Script Type |
+|---------------------|-------------|
+| "validate", "check", "verify" | Validation script |
+| "count", "tally", "aggregate", "sum" | Metric/counting script |
+| "extract", "parse", "pull from" | Data extraction script |
+| "convert", "transform", "format" | Transformation script |
+| "compare", "diff", "match against" | Comparison script |
+| "scan for", "find all", "list all" | Pattern scanning script |
+| "check structure", "verify exists" | File structure checker |
+| "against schema", "conforms to" | Schema validation script |
+| "graph", "map dependencies" | Dependency analysis script |
+
+### The Outside-the-Box Test
+Beyond obvious validation, consider:
+- Could any data gathering step be a script that returns structured JSON for the LLM to interpret?
+- Could pre-processing reduce what the LLM needs to read?
+- Could post-processing validate what the LLM produced?
+- Could metric collection feed into LLM decision-making without the LLM doing the counting?
+
+### Your Toolbox
+Scripts have access to full capabilities — think broadly:
+- **Bash**: Full shell — `jq`, `grep`, `awk`, `sed`, `find`, `diff`, `wc`, `sort`, `uniq`, `curl`, plus piping and composition
+- **Python**: Standard library (`json`, `yaml`, `pathlib`, `re`, `argparse`, `collections`, `difflib`, `ast`, `csv`, `xml`, etc.) plus PEP 723 inline-declared dependencies (`tiktoken`, `jsonschema`, `pyyaml`, etc.)
+- **System tools**: `git` commands for history/diff/blame, filesystem operations, process execution
+
+If you can express the logic as deterministic code, it's a script candidate.
+
+### The --help Pattern
+All scripts use PEP 723 and `--help`. When a skill's prompt needs to invoke a script, it can say "Run `scripts/foo.py --help` to understand inputs/outputs, then invoke appropriately" instead of inlining the script's interface. This saves tokens in prompts and keeps a single source of truth for the script's API.
+
+---
+
+## Priority 1: High-Value Validation Scripts
+
+### 1. Frontmatter Validator
+
+**What:** Validate SKILL.md frontmatter structure and content
+
+**Why:** Frontmatter is the #1 factor in skill triggering. Catch errors early.
+
+**Checks:**
+```python
+# checks:
+- name exists and is kebab-case
+- description exists and follows pattern "Use when..."
+- No forbidden fields (XML, reserved prefixes)
+- Optional fields have valid values if present
+```
+
+**Output:** JSON with pass/fail per field, line numbers for errors
+
+**Implementation:** Python with argparse, no external deps needed
+
+---
+
+### 2. Template Artifact Scanner
+
+**What:** Scan for orphaned template substitution artifacts
+
+**Why:** Build process may leave `{if-autonomous}`, `{displayName}`, etc.
+
+**Output:** JSON with file path, line number, artifact type
+
+**Implementation:** Bash script with JSON output via jq
+
+---
+
+### 3. Access Boundaries Extractor
+
+**What:** Extract and validate access boundaries from memory-system.md
+
+**Why:** Security critical — must be defined before file operations
+
+**Checks:**
+```python
+# Parse memory-system.md for:
+- ## Read Access section exists
+- ## Write Access section exists
+- ## Deny Zones section exists (can be empty)
+- Paths use placeholders correctly ({project-root} for _bmad paths, relative for skill-internal)
+```
+
+**Output:** Structured JSON of read/write/deny zones
+
+**Implementation:** Python with markdown parsing
+
+---
+
+---
+
+## Priority 2: Analysis Scripts
+
+### 4. Token Counter
+
+**What:** Count tokens in each file of an agent
+
+**Why:** Identify verbose files that need optimization
+
+**Checks:**
+```python
+# For each .md file:
+- Total tokens (approximate: chars / 4)
+- Code block tokens
+- Token density (tokens / meaningful content)
+```
+
+**Output:** JSON with file path, token count, density score
+
+**Implementation:** Python with tiktoken for accurate counting, or char approximation
+
+---
+
+### 5. Dependency Graph Generator
+
+**What:** Map skill → external skill dependencies
+
+**Why:** Understand agent's dependency surface
+
+**Checks:**
+```python
+# Parse SKILL.md for skill invocation patterns
+# Parse prompt files for external skill references
+# Build dependency graph
+```
+
+**Output:** DOT format (GraphViz) or JSON adjacency list
+
+**Implementation:** Python, JSON parsing only
+
+---
+
+### 6. Activation Flow Analyzer
+
+**What:** Parse SKILL.md On Activation section for sequence
+
+**Why:** Validate activation order matches best practices
+
+**Checks:**
+
+Validate that the activation sequence is logically ordered (e.g., config loads before config is used, memory loads before memory is referenced).
+
+**Output:** JSON with detected steps, missing steps, out-of-order warnings
+
+**Implementation:** Python with regex pattern matching
+
+---
+
+### 7. Memory Structure Validator
+
+**What:** Validate memory-system.md structure
+
+**Why:** Memory files have specific requirements
+
+**Checks:**
+```python
+# Required sections:
+- ## Core Principle
+- ## File Structure
+- ## Write Discipline
+- ## Memory Maintenance
+```
+
+**Output:** JSON with missing sections, validation errors
+
+**Implementation:** Python with markdown parsing
+
+---
+
+### 8. Subagent Pattern Detector
+
+**What:** Detect if agent uses BMAD Advanced Context Pattern
+
+**Why:** Agents processing 5+ sources MUST use subagents
+
+**Checks:**
+```python
+# Pattern detection in SKILL.md:
+- "DO NOT read sources yourself"
+- "delegate to sub-agents"
+- "/tmp/analysis-" temp file pattern
+- Sub-agent output template (50-100 token summary)
+```
+
+**Output:** JSON with pattern found/missing, recommendations
+
+**Implementation:** Python with keyword search and context extraction
+
+---
+
+## Priority 3: Composite Scripts
+
+### 9. Agent Health Check
+
+**What:** Run all validation scripts and aggregate results
+
+**Why:** One-stop shop for agent quality assessment
+
+**Composition:** Runs Priority 1 scripts, aggregates JSON outputs
+
+**Output:** Structured health report with severity levels
+
+**Implementation:** Bash script orchestrating Python scripts, jq for aggregation
+
+---
+
+### 10. Comparison Validator
+
+**What:** Compare two versions of an agent for differences
+
+**Why:** Validate changes during iteration
+
+**Checks:**
+```bash
+# Git diff with structure awareness:
+- Frontmatter changes
+- Capability additions/removals
+- New prompt files
+- Token count changes
+```
+
+**Output:** JSON with categorized changes
+
+**Implementation:** Bash with git, jq, python for analysis
+
+---
+
+## Script Output Standard
+
+All scripts MUST output structured JSON for agent consumption:
+
+```json
+{
+  "script": "script-name",
+  "version": "1.0.0",
+  "agent_path": "/path/to/agent",
+  "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": 10,
+    "critical": 1,
+    "high": 2,
+    "medium": 3,
+    "low": 4
+  }
+}
+```
+
+---
+
+## Implementation Checklist
+
+When creating validation scripts:
+
+- [ ] Uses `--help` for documentation
+- [ ] Accepts `--agent-path` for target agent
+- [ ] Outputs JSON to stdout
+- [ ] Writes diagnostics to stderr
+- [ ] Returns meaningful exit codes (0=pass, 1=fail, 2=error)
+- [ ] Includes `--verbose` flag for debugging
+- [ ] Has tests in `scripts/tests/` subfolder
+- [ ] Self-contained (PEP 723 for Python)
+- [ ] No interactive prompts
+
+---
+
+## Integration with Quality Analysis
+
+The Quality Analysis skill should:
+
+1. **First**: Run available scripts for fast, deterministic checks
+2. **Then**: Use sub-agents for semantic analysis (requires judgment)
+3. **Finally**: Synthesize both sources into report
+
+**Example flow:**
+```bash
+# Run all validation scripts
+python scripts/validate-frontmatter.py --agent-path {path}
+bash scripts/scan-template-artifacts.sh --agent-path {path}
+
+# Collect JSON outputs
+# Spawn sub-agents only for semantic checks
+# Synthesize complete report
+```
+
+---
+
+## Script Creation Priorities
+
+**Phase 1 (Immediate value):**
+1. Template Artifact Scanner (Bash + jq)
+2. Access Boundaries Extractor (Python)
+
+**Phase 2 (Enhanced validation):**
+4. Token Counter (Python)
+5. Subagent Pattern Detector (Python)
+6. Activation Flow Analyzer (Python)
+
+**Phase 3 (Advanced features):**
+7. Dependency Graph Generator (Python)
+8. Memory Structure Validator (Python)
+9. Agent Health Check orchestrator (Bash)
+
+**Phase 4 (Comparison tools):**
+10. Comparison Validator (Bash + Python)

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

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+# Skill Authoring Best Practices
+
+For field definitions and description format, see `./standard-fields.md`. For quality dimensions, see `./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`

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

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+# Standard Agent 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-agent-tech-writer`, `bmad-cis-agent-lila` |
+| `description` | [What it does]. [Use when user says 'X' or 'Y'.] | See Description Format below |
+
+## Content Fields
+
+These are used within the SKILL.md body — never in frontmatter:
+
+| Field | Description | Example |
+|-------|-------------|---------|
+| `displayName` | Friendly name (title heading, greetings) | `Paige`, `Lila`, `Floyd` |
+| `title` | Role title | `Tech Writer`, `Holodeck Operator` |
+| `icon` | Single emoji | `🔥`, `🌟` |
+| `role` | Functional role | `Technical Documentation Specialist` |
+| `sidecar` | Memory folder (optional) | `{skillName}-sidecar/` |
+
+## 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 agent does
+2. **How** — How it works (role, approach, modes)
+3. **Why/Outcome** — Value delivered, quality standard
+
+**Templates by agent type:**
+
+**Companion agents:**
+```markdown
+This skill provides a {role} who helps users {primary outcome}. Act as {displayName} — {key quality}. With {key features}, {displayName} {primary value proposition}.
+```
+
+**Workflow agents:**
+```markdown
+This skill helps you {outcome} through {approach}. Act as {role}, guiding users through {key stages/phases}. Your output is {deliverable}.
+```
+
+**Utility agents:**
+```markdown
+This skill {what it does}. Use when {when to use}. Returns {output format} with {key feature}.
+```
+
+## SKILL.md Description Format
+
+```
+{description of what the agent does}. Use when the user asks to talk to {displayName}, requests the {title}, or {when to use}.
+```
+
+## Path Rules
+
+### Skill-Internal Files
+
+All references to files within the skill use `./` relative paths:
+- `./references/memory-system.md`
+- `./references/some-guide.md`
+- `./scripts/calculate-metrics.py`
+
+This distinguishes skill-internal files from `{project-root}` paths — without the `./` prefix the LLM may confuse them.
+
+### Memory Files (sidecar)
+
+Always use `{project-root}` prefix: `{project-root}/_bmad/memory/{skillName}-sidecar/`
+
+The sidecar `index.md` is the single entry point to the agent's memory system — it tells the agent what else to load (boundaries, logs, references, etc.). Load it once on activation; don't duplicate load instructions for individual memory files.
+
+### Config Variables
+
+Use directly — they already contain `{project-root}` in their resolved values:
+- `{output_folder}/file.md`
+- Correct: `{bmad_builder_output_folder}/agent.md`
+- Wrong: `{project-root}/{bmad_builder_output_folder}/agent.md` (double-prefix)
+

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

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+# Template Substitution Rules
+
+The SKILL-template provides a minimal skeleton: frontmatter, overview, agent identity sections, sidecar, 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., `cis-`) or empty for standalone
+- `{agent-name}` → Agent functional name (kebab-case)
+- `{skill-description}` → Two parts: [4-6 word summary]. [trigger phrases]
+- `{displayName}` → Friendly display name
+- `{skillName}` → Full skill name with module prefix
+
+## Module Conditionals
+
+### For Module-Based Agents
+- `{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., `cis`)
+- `{module-setup-skill}` → Name of the module's setup skill (e.g., `bmad-cis-setup`)
+
+### For Standalone Agents
+- `{if-module}` ... `{/if-module}` → Remove the entire block including markers
+- `{if-standalone}` ... `{/if-standalone}` → Keep the content inside
+
+## Sidecar Conditionals
+
+- `{if-sidecar}` ... `{/if-sidecar}` → Keep if agent has persistent memory, otherwise remove
+- `{if-no-sidecar}` ... `{/if-no-sidecar}` → Inverse of above
+
+## Headless Conditional
+
+- `{if-headless}` ... `{/if-headless}` → Keep if agent supports headless mode, otherwise remove
+
+## Beyond the Template
+
+The builder determines the rest of the agent structure — capabilities, activation flow, sidecar initialization, capability routing, external skills, scripts — based on the agent's requirements. The template intentionally does not prescribe these.
+
+## Path References
+
+All generated agents use `./` prefix for skill-internal paths:
+- `./references/init.md` — First-run onboarding (if sidecar)
+- `./references/{capability}.md` — Individual capability prompts
+- `./references/memory-system.md` — Memory discipline (if sidecar)
+- `./scripts/` — Python/shell scripts for deterministic operations

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

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+# BMad Method · Quality Analysis Report Creator
+
+You synthesize scanner analyses into an actionable quality report for a BMad agent. 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 the agent's identity, celebrate what's strong, then show opportunities.
+
+## Inputs
+
+- `{skill-path}` — Path to the agent 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, capabilities)
+- `*-analysis.md` — LLM scanner analyses (free-form markdown)
+
+Also read the agent's `SKILL.md` to extract: name, icon, title, identity, communication style, principles, and the capability routing table.
+
+### Step 2: Build the Agent Portrait
+
+From the agent's SKILL.md, synthesize a 2-3 sentence portrait that captures who this agent is — their personality, expertise, and voice. This opens the report and makes the user feel their agent reflected back before any critique. Include the agent's icon, display name, and title.
+
+### Step 3: Build the Capability Dashboard
+
+From the routing table in SKILL.md, list every capability. Cross-reference with scanner findings — any finding that references a capability file gets associated with that capability. Rate each:
+- **Good** — no findings or only low/note severity
+- **Needs attention** — medium+ findings referencing this capability
+
+This dashboard shows the user the breadth of what they built and directs attention where it's needed.
+
+### Step 4: Synthesize Themes
+
+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
+- **Description** — what's happening and why it matters (2-3 sentences)
+- **Severity** — highest severity of constituent findings
+- **Impact** — what fixing this would improve
+- **Action** — one coherent instruction to address the root cause
+- **Constituent findings** — specific observations with source scanner, file:line, brief description
+
+Findings that don't fit any theme become standalone items in detailed analysis.
+
+### Step 5: Assess Overall Quality
+
+- **Grade:** Excellent / Good / Fair / Poor (based on severity distribution)
+- **Narrative:** 2-3 sentences capturing the agent's primary strength and primary opportunity
+
+### Step 6: Collect Strengths
+
+Gather strengths from all scanners. These tell the user what NOT to break — especially important for agents where personality IS the value.
+
+### Step 7: Organize Detailed Analysis
+
+For each analysis dimension, summarize the scanner's assessment and list findings not covered by themes:
+- **Structure & Capabilities** — from structure scanner
+- **Persona & Voice** — from prompt-craft scanner (agent-specific framing)
+- **Identity Cohesion** — from agent-cohesion scanner
+- **Execution Efficiency** — from execution-efficiency scanner
+- **Conversation Experience** — from enhancement-opportunities scanner (journeys, headless, edge cases)
+- **Script Opportunities** — from script-opportunities scanner
+
+### Step 8: 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.
+
+## Write Two Files
+
+### 1. quality-report.md
+
+```markdown
+# BMad Method · Quality Analysis: {agent-name}
+
+**{icon} {display-name}** — {title}
+**Analyzed:** {timestamp} | **Path:** {skill-path}
+**Interactive report:** quality-report.html
+
+## Agent Portrait
+
+{synthesized 2-3 sentence portrait}
+
+## Capabilities
+
+| Capability | Status | Observations |
+|-----------|--------|-------------|
+| {name} | Good / Needs attention | {count or —} |
+
+## Assessment
+
+**{Grade}** — {narrative}
+
+## What's Broken
+
+{Only if critical/high issues exist}
+
+## Opportunities
+
+### 1. {Theme Name} ({severity} — {N} observations)
+
+{Description + Fix + constituent findings}
+
+## Strengths
+
+{What this agent does well}
+
+## Detailed Analysis
+
+### Structure & Capabilities
+### Persona & Voice
+### Identity Cohesion
+### Execution Efficiency
+### Conversation Experience
+### Script Opportunities
+
+## Recommendations
+
+1. {Highest impact}
+2. ...
+```
+
+### 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-agent-name",
+    "skill_path": "/full/path/to/agent",
+    "timestamp": "2026-03-26T23:03:03Z",
+    "scanner_count": 8,
+    "type": "agent"
+  },
+  "agent_profile": {
+    "icon": "emoji icon from agent's SKILL.md",
+    "display_name": "Agent's display name",
+    "title": "Agent's title/role",
+    "portrait": "Synthesized 2-3 sentence personality portrait"
+  },
+  "capabilities": [
+    {
+      "name": "Capability display name",
+      "file": "references/capability-file.md",
+      "status": "good|needs-attention",
+      "finding_count": 0,
+      "findings": [
+        {
+          "title": "Observation about this capability",
+          "severity": "medium",
+          "source": "which-scanner"
+        }
+      ]
+    }
+  ],
+  "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",
+      "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",
+      "findings": []
+    },
+    "persona": {
+      "assessment": "1-3 sentence summary",
+      "overview_quality": "appropriate|excessive|missing",
+      "findings": []
+    },
+    "cohesion": {
+      "assessment": "1-3 sentence summary",
+      "dimensions": {
+        "persona_capability_alignment": { "score": "strong|moderate|weak", "notes": "explanation" }
+      },
+      "findings": []
+    },
+    "efficiency": {
+      "assessment": "1-3 sentence summary",
+      "findings": []
+    },
+    "experience": {
+      "assessment": "1-3 sentence summary",
+      "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 agent shines"]
+        }
+      ],
+      "autonomous": {
+        "potential": "headless-ready|easily-adaptable|partially-adaptable|fundamentally-interactive",
+        "notes": "Brief assessment"
+      },
+      "findings": []
+    },
+    "scripts": {
+      "assessment": "1-3 sentence summary",
+      "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)?
+3. Does `agent_profile` have all 4 fields: `icon`, `display_name`, `title`, `portrait`?
+4. Is every strength an object `{"title": "...", "detail": "..."}` (not a plain string)?
+5. Does every opportunity use `name` (not `title`) and include `finding_count` and `findings` array?
+6. Does every recommendation use `action` (not `description`) and include `rank` number?
+7. Does every capability include `name`, `file`, `status`, `finding_count`, `findings`?
+8. Are detailed_analysis keys exactly: `structure`, `persona`, `cohesion`, `efficiency`, `experience`, `scripts`?
+9. Does every journey use `archetype` (not `persona`), `summary` (not `friction`), `friction_points` array, `bright_spots` array?
+10. 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 and tell the story of this agent — who it is, what it does well, and what would make it even better. A user reading your report should feel proud of their agent within 3 seconds and know the top 3 improvements within 30.

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

@@ -0,0 +1,534 @@
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+#!/usr/bin/env python3
+"""
+Generate an interactive HTML quality analysis report for a BMad agent.
+
+Reads report-data.json produced by the report creator and renders a
+self-contained HTML report with:
+  - BMad Method branding
+  - Agent portrait (icon, name, title, personality description)
+  - Capability dashboard with expandable per-capability findings
+  - Opportunity themes with "Fix This Theme" prompt generation
+  - Expandable strengths and detailed analysis
+
+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'))
+
+
+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;
+  --brand: #a371f7;
+  --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;
+    --brand: #8250df;
+  }
+}
+* { 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; }
+.brand { color: var(--brand); font-size: 0.8rem; font-weight: 600; letter-spacing: 0.05em; text-transform: uppercase; margin-bottom: 0.25rem; }
+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; }
+.portrait { background: var(--surface); border: 1px solid var(--border); border-radius: 0.5rem; padding: 1.25rem; margin-bottom: 1.5rem; }
+.portrait-header { display: flex; align-items: center; gap: 0.75rem; margin-bottom: 0.5rem; }
+.portrait-icon { font-size: 2rem; }
+.portrait-name { font-size: 1.25rem; font-weight: 700; }
+.portrait-title { font-size: 0.9rem; color: var(--text-muted); }
+.portrait-desc { font-size: 0.95rem; color: var(--text-muted); line-height: 1.6; font-style: italic; }
+.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); }
+.badge-good { background: color-mix(in srgb, var(--strength) 15%, transparent); color: var(--strength); }
+.badge-attention { background: color-mix(in srgb, var(--medium) 15%, transparent); color: var(--medium); }
+.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 .actions { display: flex; gap: 0.5rem; }
+.section-body { display: none; }
+.section-body.open { display: block; }
+.cap-row { display: flex; align-items: center; gap: 0.75rem; padding: 0.6rem 1rem; border-top: 1px solid var(--border); }
+.cap-row:hover { background: var(--surface); }
+.cap-name { font-weight: 600; font-size: 0.9rem; flex: 1; }
+.cap-file { font-family: var(--mono); font-size: 0.75rem; color: var(--text-dim); }
+.cap-findings { display: none; padding: 0.5rem 1rem 0.5rem 2rem; border-top: 1px solid var(--border); background: var(--bg); }
+.cap-findings.open { display: block; }
+.cap-finding { font-size: 0.85rem; padding: 0.25rem 0; color: var(--text-muted); }
+.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; flex-wrap: wrap; }
+.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); }
+.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); }
+.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); }
+.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; }
+</style>
+</head>
+<body>
+
+<div class="brand">BMad Method</div>
+<h1>Quality Analysis: <span id="skill-name"></span></h1>
+<div class="subtitle" id="subtitle"></div>
+
+<div id="portrait"></div>
+<div id="grade-area"></div>
+<div class="narrative" id="narrative"></div>
+
+<div id="capabilities-section"></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) {
+  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;
+  }
+  d.strengths = (d.strengths || []).map(s =>
+    typeof s === 'string' ? { title: s, detail: '' } : { title: s.title || '', detail: s.detail || '' }
+  );
+  (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 || '';
+  });
+  (d.broken || []).forEach(b => {
+    b.detail = b.detail || b.description || '';
+    b.action = b.action || b.fix || '';
+  });
+  (d.recommendations || []).forEach((r, i) => {
+    r.action = r.action || r.description || '';
+    r.rank = r.rank || i + 1;
+  });
+  // Fix journeys
+  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] : [])
+    }));
+  }
+  // Fix capabilities
+  (d.capabilities || []).forEach(c => {
+    c.finding_count = c.finding_count || (c.findings || []).length;
+    c.status = c.status || (c.finding_count > 0 ? 'needs-attention' : 'good');
+  });
+  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; ${m.scanner_count || 0} scanners &bull; <a href="quality-report.md">Full Report &nearr;</a>`;
+
+  renderPortrait();
+  document.getElementById('grade-area').innerHTML = `<div class="grade grade-${DATA.grade}">${esc(DATA.grade)}</div>`;
+  document.getElementById('narrative').textContent = DATA.narrative || '';
+
+  renderCapabilities();
+  renderBroken();
+  renderOpportunities();
+  renderStrengths();
+  renderRecommendations();
+  renderDetailed();
+}
+
+function renderPortrait() {
+  const p = DATA.agent_profile;
+  if (!p) return;
+  let html = `<div class="portrait"><div class="portrait-header">`;
+  if (p.icon) html += `<span class="portrait-icon">${esc(p.icon)}</span>`;
+  html += `<div><div class="portrait-name">${esc(p.display_name)}</div>`;
+  if (p.title) html += `<div class="portrait-title">${esc(p.title)}</div>`;
+  html += `</div></div>`;
+  if (p.portrait) html += `<div class="portrait-desc">${esc(p.portrait)}</div>`;
+  html += `</div>`;
+  document.getElementById('portrait').innerHTML = html;
+}
+
+function renderCapabilities() {
+  const caps = DATA.capabilities || [];
+  if (!caps.length) return;
+  const good = caps.filter(c => c.status === 'good').length;
+  const attn = caps.length - good;
+  let summary = `${caps.length} capabilities`;
+  if (attn > 0) summary += ` \u00b7 ${attn} need attention`;
+
+  let html = `<div class="section"><div class="section-header open" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Capabilities (${summary})</span>`;
+  html += `</div><div class="section-body open">`;
+  caps.forEach((cap, idx) => {
+    const statusBadge = cap.status === 'good'
+      ? `<span class="badge badge-good">Good</span>`
+      : `<span class="badge badge-attention">${cap.finding_count} observation${cap.finding_count !== 1 ? 's' : ''}</span>`;
+    const hasFindings = cap.findings && cap.findings.length > 0;
+    html += `<div class="cap-row" ${hasFindings ? `onclick="toggleCapFindings(${idx})" style="cursor:pointer"` : ''}>`;
+    html += `${statusBadge} <span class="cap-name">${esc(cap.name)}</span>`;
+    if (cap.file) html += `<span class="cap-file">${esc(cap.file)}</span>`;
+    html += `</div>`;
+    if (hasFindings) {
+      html += `<div class="cap-findings" id="cap-findings-${idx}">`;
+      cap.findings.forEach(f => {
+        html += `<div class="cap-finding">`;
+        if (f.severity) html += `<span class="badge badge-${f.severity}">${esc(f.severity)}</span> `;
+        html += `${esc(f.title)}`;
+        if (f.source) html += ` <span class="source" style="font-size:0.75rem;color:var(--text-dim)">[${esc(f.source)}]</span>`;
+        html += `</div>`;
+      });
+      html += `</div>`;
+    }
+  });
+  html += `</div></div>`;
+  document.getElementById('capabilities-section').innerHTML = html;
+}
+
+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}${item.line ? ':'+item.line : ''}` : '';
+    html += `<div class="item"><span class="badge badge-${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"><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"><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">${esc(s.detail)}</div>`;
+    html += `</div>`;
+  });
+  html += `</div></div>`;
+  document.getElementById('strengths-section').innerHTML = html;
+}
+
+function renderRecommendations() {
+  const recs = DATA.recommendations || [];
+  if (!recs.length) return;
+  let html = `<div class="section"><div class="section-header open" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Recommendations</span>`;
+  html += `</div><div class="section-body open"><div class="recs">`;
+  recs.forEach(r => {
+    html += `<div class="rec"><span class="rec-rank">#${r.rank}</span>${esc(r.action)}`;
+    if (r.resolves) html += ` <span class="rec-resolves">(resolves ${r.resolves} observations)</span>`;
+    html += `</div>`;
+  });
+  html += `</div></div></div>`;
+  document.getElementById('recommendations-section').innerHTML = html;
+}
+
+function renderDetailed() {
+  const da = DATA.detailed_analysis;
+  if (!da) return;
+  const dims = [
+    ['structure', 'Structure & Capabilities'],
+    ['persona', 'Persona & Voice'],
+    ['cohesion', 'Identity Cohesion'],
+    ['efficiency', 'Execution Efficiency'],
+    ['experience', 'Conversation Experience'],
+    ['scripts', 'Script Opportunities']
+  ];
+  let html = `<div class="section"><div class="section-header" onclick="toggleSection(this)">`;
+  html += `<span class="arrow">&#9654;</span><span class="label">Detailed Analysis</span>`;
+  html += `</div><div class="section-body">`;
+  dims.forEach(([key, label]) => {
+    const dim = da[key];
+    if (!dim) return;
+    html += `<div class="analysis-section"><h4>${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 += ` \u2014 ${esc(a.notes)}`;
+      html += `</p>`;
+    }
+    (dim.findings || []).forEach(f => {
+      const loc = f.file ? `${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;
+}
+
+function toggleSection(el) { el.classList.toggle('open'); el.nextElementSibling.classList.toggle('open'); }
+function toggleFindings(idx) { document.getElementById('findings-'+idx).classList.toggle('open'); }
+function toggleCapFindings(idx) { document.getElementById('cap-findings-'+idx).classList.toggle('open'); }
+
+function showThemePrompt(idx) {
+  const opp = DATA.opportunities[idx];
+  if (!opp) return;
+  let prompt = `## Task: ${opp.name}\nAgent path: ${DATA.meta.skill_path}\n\n### Problem\n${opp.description}\n\n### 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\nAgent 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\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() {
+  navigator.clipboard.writeText(document.getElementById('modal-content').textContent).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:
+    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 for a BMad agent')
+    parser.add_argument('report_dir', type=Path, help='Directory containing report-data.json')
+    parser.add_argument('--open', action='store_true', help='Open in default browser')
+    parser.add_argument('--output', '-o', type=Path, help='Output HTML file path')
+    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')
+
+    print(json.dumps({
+        'html_report': str(output_path),
+        'grade': report_data.get('grade', 'Unknown'),
+        'opportunities': len(report_data.get('opportunities', [])),
+        'broken': len(report_data.get('broken', [])),
+    }))
+
+    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())

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

@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for execution efficiency scanner (agent builder).
+
+Extracts dependency graph data and execution patterns from a BMad agent 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
+- Loop patterns (read all, analyze each, for each file)
+- Memory loading pattern detection (load all memory, read all sidecar, etc.)
+- Multi-source operation 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)."""
+    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',
+            })
+
+    # Memory loading patterns (agent-specific)
+    memory_loading_patterns = [
+        (r'[Ll]oad all (?:memory|memories)', 'load-all-memory'),
+        (r'[Rr]ead all sidecar (?:files|data)', 'read-all-sidecar'),
+        (r'[Ll]oad (?:entire|full|complete) sidecar', 'load-entire-sidecar'),
+        (r'[Ll]oad all (?:context|state)', 'load-all-context'),
+        (r'[Rr]ead (?:entire|full|complete) memory', 'read-entire-memory'),
+    ]
+    for pattern, ptype in memory_loading_patterns:
+        matches = re.findall(pattern, content)
+        if matches:
+            patterns.append({
+                'file': rel_path,
+                'type': ptype,
+                'count': len(matches),
+                'issue': f'"{matches[0]}" pattern found — bulk memory loading is expensive, load specific paths',
+            })
+
+    # Multi-source operation detection (agent-specific)
+    multi_source_patterns = [
+        (r'[Rr]ead all\b', 'multi-source-read-all'),
+        (r'[Aa]nalyze each\b', 'multi-source-analyze-each'),
+        (r'[Ff]or each file\b', 'multi-source-for-each-file'),
+    ]
+    for pattern, ptype in multi_source_patterns:
+        matches = re.findall(pattern, content)
+        if matches:
+            # Only add if not already captured by loop_patterns above
+            existing_types = {p['type'] for p in patterns}
+            if ptype not in existing_types:
+                patterns.append({
+                    'file': rel_path,
+                    'type': ptype,
+                    'count': len(matches),
+                    'issue': f'"{matches[0]}" pattern found — multi-source operation may be parallelizable',
+                })
+
+    # Subagent spawning from subagent (impossible)
+    if re.search(r'(?i)spawn.*subagent|launch.*subagent|create.*subagent', content):
+        # Check if this file IS a subagent (quality-scan-* or report-* files at root)
+        if re.match(r'(?:quality-scan-|report-)', 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 definitions in prompt files
+    prompts_dir = skill_path / 'prompts'
+    if prompts_dir.exists():
+        for f in sorted(prompts_dir.iterdir()):
+            if f.is_file() and f.suffix == '.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
+    sequential_patterns = []
+    for scan_dir in ['prompts', 'agents']:
+        d = skill_path / scan_dir
+        if d.exists():
+            for f in sorted(d.iterdir()):
+                if f.is_file() and f.suffix == '.md':
+                    patterns = scan_sequential_patterns(f, f'{scan_dir}/{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:
+        if p['type'] == 'subagent-chain-violation':
+            severity = 'critical'
+        elif p['type'] in ('load-all-memory', 'read-all-sidecar', 'load-entire-sidecar',
+                           'load-all-context', 'read-entire-memory'):
+            severity = 'high'
+        else:
+            severity = '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['high'] > 0 or 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 (agent builder)',
+    )
+    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())

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

@@ -0,0 +1,403 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for prompt craft scanner (agent builder).
+
+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)
+- Prompt frontmatter validation (name, description, menu-code)
+- Wall-of-text detection
+- Suggestive loading grep
+"""
+
+# /// 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"'),
+]
+
+# Suggestive loading patterns
+SUGGESTIVE_LOADING_PATTERNS = [
+    (r'\b[Ll]oad (?:the |all )?(?:relevant|necessary|needed|required)\b', 'Suggestive loading: "load relevant/necessary"'),
+    (r'\b[Rr]ead (?:the |all )?(?:relevant|necessary|needed|required)\b', 'Suggestive loading: "read relevant/necessary"'),
+    (r'\b[Gg]ather (?:the |all )?(?:relevant|necessary|needed)\b', 'Suggestive loading: "gather relevant/necessary"'),
+]
+
+
+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 detect_wall_of_text(content: str) -> list[dict]:
+    """Detect long runs of text without headers or breaks."""
+    walls = []
+    lines = content.split('\n')
+    run_start = None
+    run_length = 0
+
+    for i, line in enumerate(lines, 1):
+        stripped = line.strip()
+        is_break = (
+            not stripped
+            or re.match(r'^#{1,6}\s', stripped)
+            or re.match(r'^[-*]\s', stripped)
+            or re.match(r'^\d+\.\s', stripped)
+            or stripped.startswith('```')
+            or stripped.startswith('|')
+        )
+
+        if is_break:
+            if run_length >= 15:
+                walls.append({
+                    'start_line': run_start,
+                    'length': run_length,
+                })
+            run_start = None
+            run_length = 0
+        else:
+            if run_start is None:
+                run_start = i
+            run_length += 1
+
+    if run_length >= 15:
+        walls.append({
+            'start_line': run_start,
+            'length': run_length,
+        })
+
+    return walls
+
+
+def parse_prompt_frontmatter(filepath: Path) -> dict:
+    """Parse YAML frontmatter from a prompt file and validate."""
+    content = filepath.read_text(encoding='utf-8')
+    result = {
+        'has_frontmatter': False,
+        'fields': {},
+        'missing_fields': [],
+    }
+
+    fm_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL)
+    if not fm_match:
+        result['missing_fields'] = ['name', 'description', 'menu-code']
+        return result
+
+    result['has_frontmatter'] = True
+
+    try:
+        import yaml
+        fm = yaml.safe_load(fm_match.group(1))
+    except Exception:
+        # Fallback: simple key-value parsing
+        fm = {}
+        for line in fm_match.group(1).split('\n'):
+            if ':' in line:
+                key, _, val = line.partition(':')
+                fm[key.strip()] = val.strip()
+
+    if not isinstance(fm, dict):
+        result['missing_fields'] = ['name', 'description', 'menu-code']
+        return result
+
+    expected_fields = ['name', 'description', 'menu-code']
+    for field in expected_fields:
+        if field in fm:
+            result['fields'][field] = fm[field]
+        else:
+            result['missing_fields'].append(field)
+
+    return result
+
+
+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],
+            })
+
+    # Suggestive loading
+    suggestive_loading = []
+    for pattern, label in SUGGESTIVE_LOADING_PATTERNS:
+        for m in re.finditer(pattern, content, re.IGNORECASE):
+            line_num = content[:m.start()].count('\n') + 1
+            suggestive_loading.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)
+
+    # Wall-of-text detection
+    walls = detect_wall_of_text(content)
+
+    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,
+        'suggestive_loading': suggestive_loading,
+        'has_config_header': has_config_header,
+        'has_progression': has_progression,
+        'wall_of_text': walls,
+    }
+
+    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
+    skip_files = {'SKILL.md'}
+
+    for f in sorted(skill_path.iterdir()):
+        if f.is_file() and f.suffix == '.md' and f.name not in skip_files and f.name != 'SKILL.md':
+            data = scan_file_patterns(f, f.name)
+            data['is_skill_md'] = False
+
+            # Parse prompt frontmatter
+            pfm = parse_prompt_frontmatter(f)
+            data['prompt_frontmatter'] = pfm
+
+            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_suggestive = sum(len(f.get('suggestive_loading', [])) for f in files_data)
+    total_tokens = sum(f['token_estimate'] for f in files_data)
+    total_walls = sum(len(f.get('wall_of_text', [])) 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,
+            'total_suggestive_loading': total_suggestive,
+            'total_wall_of_text': total_walls,
+        },
+        'resource_sizes': resource_sizes,
+        'files': files_data,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Extract prompt craft metrics for LLM scanner pre-pass (agent builder)',
+    )
+    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())

.opencode/skills/bmad-agent-builder/scripts/prepass-structure-capabilities.py

@@ -0,0 +1,445 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for agent structure and capabilities scanner.
+
+Extracts structural metadata from a BMad agent 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
+- Agent name validation (bmad-{code}-agent-{name} or bmad-agent-{name})
+- Required agent sections (Overview, Identity, Communication Style, Principles, On Activation)
+- Memory path consistency checking
+- Language/directness pattern grep
+- On Exit / Exiting section detection (invalid)
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# dependencies = [
+#     "pyyaml>=6.0",
+# ]
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+try:
+    import yaml
+except ImportError:
+    print("Error: pyyaml required. Run with: uv run prepass-structure-capabilities.py", file=sys.stderr)
+    sys.exit(2)
+
+
+# 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'\{if-autonomous\}', r'\{/if-autonomous\}',
+    r'\{if-sidecar\}', r'\{/if-sidecar\}',
+    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}',
+    '{headless_mode}',
+}
+
+# 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:
+        fm = yaml.safe_load(fm_match.group(1))
+    except yaml.YAMLError as e:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'critical', 'category': 'frontmatter',
+            'issue': f'Invalid YAML 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]+)*$', name):
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'frontmatter',
+            'issue': f'Name "{name}" is not kebab-case',
+        })
+    elif not (re.match(r'^bmad-[a-z0-9]+-agent-[a-z0-9]+(-[a-z0-9]+)*$', name)
+              or re.match(r'^bmad-agent-[a-z0-9]+(-[a-z0-9]+)*$', name)):
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'medium', 'category': 'frontmatter',
+            'issue': f'Name "{name}" does not follow bmad-{{code}}-agent-{{name}} or bmad-agent-{{name}} pattern',
+        })
+
+    # description check
+    desc = fm.get('description')
+    if not desc:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'high', 'category': 'frontmatter',
+            'issue': 'Missing "description" field in frontmatter',
+        })
+    elif 'Use when' not in desc and 'use when' not in desc:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'medium', 'category': 'frontmatter',
+            'issue': 'Description missing "Use when..." trigger phrase',
+        })
+
+    # Extra fields check — only name and description allowed for agents
+    allowed = {'name', 'description'}
+    extra = set(fm.keys()) - allowed
+    if extra:
+        findings.append({
+            'file': 'SKILL.md', 'line': 1,
+            'severity': 'low', 'category': 'frontmatter',
+            'issue': f'Extra frontmatter fields: {", ".join(sorted(extra))}',
+        })
+
+    return fm, findings
+
+
+def extract_sections(content: str) -> list[dict]:
+    """Extract all H2/H3 headers with line numbers."""
+    sections = []
+    for i, line in enumerate(content.split('\n'), 1):
+        m = re.match(r'^(#{2,3})\s+(.+)$', 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]
+
+    required = ['Overview', 'Identity', 'Communication Style', 'Principles', 'On Activation']
+    for req in required:
+        if req not in h2_titles:
+            findings.append({
+                'file': 'SKILL.md', 'line': 1,
+                'severity': 'high', 'category': 'sections',
+                'issue': f'Missing ## {req} 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 extract_memory_paths(skill_path: Path) -> tuple[list[str], list[dict]]:
+    """Extract all memory path references across files and check consistency."""
+    findings = []
+    memory_paths = set()
+
+    # Memory path patterns
+    mem_pattern = re.compile(r'(?:memory/|sidecar/)[\w\-/]+(?:\.\w+)?')
+
+    files_to_scan = []
+
+    skill_md = skill_path / 'SKILL.md'
+    if skill_md.exists():
+        files_to_scan.append(('SKILL.md', skill_md))
+
+    for subdir in ['prompts', 'resources']:
+        d = skill_path / subdir
+        if d.exists():
+            for f in sorted(d.iterdir()):
+                if f.is_file() and f.suffix in ('.md', '.json', '.yaml', '.yml'):
+                    files_to_scan.append((f'{subdir}/{f.name}', f))
+
+    for rel_path, filepath in files_to_scan:
+        content = filepath.read_text(encoding='utf-8')
+        for m in mem_pattern.finditer(content):
+            memory_paths.add(m.group())
+
+    sorted_paths = sorted(memory_paths)
+
+    # Check for inconsistent formats
+    prefixes = set()
+    for p in sorted_paths:
+        prefix = p.split('/')[0]
+        prefixes.add(prefix)
+
+    memory_prefixes = {p for p in prefixes if 'memory' in p.lower()}
+    sidecar_prefixes = {p for p in prefixes if 'sidecar' in p.lower()}
+
+    if len(memory_prefixes) > 1:
+        findings.append({
+            'file': 'multiple', 'line': 0,
+            'severity': 'medium', 'category': 'memory-paths',
+            'issue': f'Inconsistent memory path prefixes: {", ".join(sorted(memory_prefixes))}',
+        })
+
+    if len(sidecar_prefixes) > 1:
+        findings.append({
+            'file': 'multiple', 'line': 0,
+            'severity': 'medium', 'category': 'memory-paths',
+            'issue': f'Inconsistent sidecar path prefixes: {", ".join(sorted(sidecar_prefixes))}',
+        })
+
+    return sorted_paths, 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 = []
+    skip_files = {'SKILL.md'}
+
+    prompt_files = [f for f in sorted(skill_path.iterdir())
+                    if f.is_file() and f.suffix == '.md' and f.name not in skip_files]
+    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
+        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 scan_structure_capabilities(skill_path: Path) -> dict:
+    """Run all deterministic agent structure and capability checks."""
+    all_findings = []
+
+    # Read SKILL.md
+    skill_md = skill_path / 'SKILL.md'
+    if not skill_md.exists():
+        return {
+            'scanner': 'structure-capabilities-prepass',
+            'script': 'prepass-structure-capabilities.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,
+            })
+
+    # Memory path consistency
+    memory_paths, memory_findings = extract_memory_paths(skill_path)
+    all_findings.extend(memory_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': 'structure-capabilities-prepass',
+        'script': 'prepass-structure-capabilities.py',
+        'version': '1.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': status,
+        'metadata': {
+            'frontmatter': frontmatter,
+            'sections': sections,
+        },
+        'prompt_details': prompt_details,
+        'memory_paths': memory_paths,
+        'issues': all_findings,
+        'summary': {
+            'total_issues': len(all_findings),
+            'by_severity': by_severity,
+        },
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Deterministic pre-pass for agent structure and capabilities 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_structure_capabilities(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())

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

@@ -0,0 +1,339 @@
+#!/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. Memory paths must use {project-root}/_bmad/memory/{skillName}-sidecar/
+8. Frontmatter allows only name and description
+9. 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)
+
+# Memory path pattern: should use {project-root}/_bmad/memory/
+MEMORY_PATH_RE = re.compile(r'_bmad/memory/\S+')
+VALID_MEMORY_PATH_RE = re.compile(r'\{project-root\}/_bmad/memory/\S+-sidecar/')
+
+# 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': '',
+        })
+
+    # Memory path check — memory paths should use {project-root}/_bmad/memory/{skillName}-sidecar/
+    for match in MEMORY_PATH_RE.finditer(content):
+        pos = match.start()
+        if skip_fenced and is_in_fenced_block(content, pos):
+            continue
+        start = max(0, pos - 20)
+        before = content[start:pos]
+        matched_text = match.group()
+        if '{project-root}/' not in before:
+            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': 'memory-path',
+                'title': 'Memory path missing {project-root} prefix — use {project-root}/_bmad/memory/',
+                'detail': line_content[:120],
+                'action': '',
+            })
+        elif '-sidecar/' not in matched_text:
+            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': 'memory-path',
+                'title': 'Memory path not using {skillName}-sidecar/ convention',
+                '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,
+        'memory_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())

.opencode/skills/bmad-agent-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())