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
This commit is contained in:
Sepehr Ramezani
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.claude/skills/bmad-agent-builder/quality-analysis.md
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name: quality-analysis
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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.
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menu-code: QA
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---
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**Language:** Use `{communication_language}` for all output.
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# BMad Method · Quality Analysis
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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.
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## Your Role
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**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.
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## Headless Mode
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If `{headless_mode}=true`, skip all user interaction, use safe defaults, note warnings, and output structured JSON as specified in Present to User.
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## Pre-Scan Checks
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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.
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## Analysis Principles
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**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.
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## Scanners
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### Lint Scripts (Deterministic — Run First)
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| # | Script | Focus | Output File |
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|---|--------|-------|-------------|
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| S1 | `scripts/scan-path-standards.py` | Path conventions | `path-standards-temp.json` |
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| S2 | `scripts/scan-scripts.py` | Script portability, PEP 723, unit tests | `scripts-temp.json` |
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### Pre-Pass Scripts (Feed LLM Scanners)
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| # | Script | Feeds | Output File |
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|---|--------|-------|-------------|
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| P1 | `scripts/prepass-structure-capabilities.py` | structure scanner | `structure-capabilities-prepass.json` |
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| P2 | `scripts/prepass-prompt-metrics.py` | prompt-craft scanner | `prompt-metrics-prepass.json` |
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| P3 | `scripts/prepass-execution-deps.py` | execution-efficiency scanner | `execution-deps-prepass.json` |
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### LLM Scanners (Judgment-Based — Run After Scripts)
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Each scanner writes a free-form analysis document:
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| # | Scanner | Focus | Pre-Pass? | Output File |
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|---|---------|-------|-----------|-------------|
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| L1 | `quality-scan-structure.md` | Structure, capabilities, identity, memory, consistency | Yes | `structure-analysis.md` |
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| L2 | `quality-scan-prompt-craft.md` | Token efficiency, outcome balance, persona voice, per-capability craft | Yes | `prompt-craft-analysis.md` |
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| L3 | `quality-scan-execution-efficiency.md` | Parallelization, delegation, memory loading, context optimization | Yes | `execution-efficiency-analysis.md` |
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| L4 | `quality-scan-agent-cohesion.md` | Persona-capability alignment, identity coherence, per-capability cohesion | No | `agent-cohesion-analysis.md` |
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| L5 | `quality-scan-enhancement-opportunities.md` | Edge cases, experience gaps, user journeys, headless potential | No | `enhancement-opportunities-analysis.md` |
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| L6 | `quality-scan-script-opportunities.md` | Deterministic operations that should be scripts | No | `script-opportunities-analysis.md` |
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## Execution
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First create output directory: `{bmad_builder_reports}/{skill-name}/quality-analysis/{date-time-stamp}/`
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### Step 1: Run All Scripts (Parallel)
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```bash
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python3 scripts/scan-path-standards.py {skill-path} -o {report-dir}/path-standards-temp.json
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python3 scripts/scan-scripts.py {skill-path} -o {report-dir}/scripts-temp.json
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python3 scripts/prepass-structure-capabilities.py {skill-path} -o {report-dir}/structure-capabilities-prepass.json
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python3 scripts/prepass-prompt-metrics.py {skill-path} -o {report-dir}/prompt-metrics-prepass.json
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uv run scripts/prepass-execution-deps.py {skill-path} -o {report-dir}/execution-deps-prepass.json
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```
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### Step 2: Spawn LLM Scanners (Parallel)
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After scripts complete, spawn all scanners as parallel subagents.
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**With pre-pass (L1, L2, L3):** provide pre-pass JSON path.
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**Without pre-pass (L4, L5, L6):** provide skill path and output directory.
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Each subagent loads the scanner file, analyzes the agent, writes analysis to the output directory, returns the filename.
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### Step 3: Synthesize Report
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Spawn a subagent with `report-quality-scan-creator.md`.
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Provide:
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- `{skill-path}` — The agent being analyzed
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- `{quality-report-dir}` — Directory with all scanner output
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The report creator reads everything, synthesizes agent portrait + capability dashboard + themes, writes:
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1. `quality-report.md` — Narrative markdown with BMad Method branding
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2. `report-data.json` — Structured data for HTML
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### Step 4: Generate HTML Report
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```bash
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python3 scripts/generate-html-report.py {report-dir} --open
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```
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## Present to User
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**IF `{headless_mode}=true`:**
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Read `report-data.json` and output:
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```json
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{
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"headless_mode": true,
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"scan_completed": true,
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"report_file": "{path}/quality-report.md",
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"html_report": "{path}/quality-report.html",
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"data_file": "{path}/report-data.json",
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"grade": "Excellent|Good|Fair|Poor",
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"opportunities": 0,
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"broken": 0
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}
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```
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**IF interactive:**
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Read `report-data.json` and present:
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1. Agent portrait — icon, name, title
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2. Grade and narrative
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3. Capability dashboard summary
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4. Top opportunities
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5. Reports — paths and "HTML opened in browser"
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6. Offer: apply fixes, use HTML to select items, discuss findings
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