920e8547af
- Add commands/plugin-audit.md (distributable) + .claude/commands/plugin-audit.md (local invocation) - 8 phases: discovery, structure validation, quality scoring, script testing, security audit, marketplace compliance, ecosystem integration, domain code review - Auto-fixes non-critical issues, only prompts user for breaking changes - Integrates skill_validator.py, quality_scorer.py, script_tester.py, skill_security_auditor.py - Domain-appropriate review via cs-* agents (engineering, product, marketing, etc.) - Update product-team counts: 12→14 skills, 13→16 tools, 7→8 commands - Add /code-to-prd and /plugin-audit to mkdocs.yml nav - Regenerate docs (248 pages, 19 commands) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
85 lines
2.8 KiB
Markdown
85 lines
2.8 KiB
Markdown
---
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title: "/code-to-prd — Slash Command for AI Coding Agents"
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description: "Reverse-engineer a frontend codebase into a PRD. Usage: /code-to-prd [path]. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /code-to-prd
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-console: Slash Command</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/commands/code-to-prd.md">Source</a></span>
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</div>
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Reverse-engineer a frontend codebase into a complete Product Requirements Document.
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## Usage
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```bash
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/code-to-prd # Analyze current project
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/code-to-prd ./src # Analyze specific directory
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/code-to-prd /path/to/project # Analyze external project
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```
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## What It Does
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1. **Scan** — Run `codebase_analyzer.py` to detect framework, routes, APIs, enums, and project structure
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2. **Scaffold** — Run `prd_scaffolder.py` to create `prd/` directory with README.md, per-page stubs, and appendix files
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3. **Analyze** — Walk through each page following the Phase 2 workflow: fields, interactions, API dependencies, page relationships
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4. **Generate** — Produce the final PRD with all pages, enum dictionary, API inventory, and page relationship map
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## Steps
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### Step 1: Analyze
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Determine the project path (default: current directory). Run the frontend analyzer:
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```bash
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python3 {skill_path}/scripts/codebase_analyzer.py {project_path} -o .code-to-prd-analysis.json
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```
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Display a summary of findings: framework, page count, API count, enum count.
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### Step 2: Scaffold
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Generate the PRD directory skeleton:
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```bash
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python3 {skill_path}/scripts/prd_scaffolder.py .code-to-prd-analysis.json -o prd/
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```
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### Step 3: Fill
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For each page in the inventory, follow the SKILL.md Phase 2 workflow:
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- Read the page's component files
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- Document fields, interactions, API dependencies, page relationships
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- Fill in the corresponding `prd/pages/` stub
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Work in batches of 3-5 pages for large projects (>15 pages). Ask the user to confirm after each batch.
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### Step 4: Finalize
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Complete the appendix files:
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- `prd/appendix/enum-dictionary.md` — all enums and status codes found
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- `prd/appendix/api-inventory.md` — consolidated API reference
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- `prd/appendix/page-relationships.md` — navigation and data coupling map
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Clean up the temporary analysis file:
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```bash
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rm .code-to-prd-analysis.json
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```
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## Output
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A `prd/` directory containing:
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- `README.md` — system overview, module map, page inventory
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- `pages/*.md` — one file per page with fields, interactions, APIs
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- `appendix/*.md` — enum dictionary, API inventory, page relationships
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## Skill Reference
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- `product-team/code-to-prd/SKILL.md`
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- `product-team/code-to-prd/scripts/codebase_analyzer.py`
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- `product-team/code-to-prd/scripts/prd_scaffolder.py`
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- `product-team/code-to-prd/references/prd-quality-checklist.md`
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