firefrost-gaming/claude-skills-reference

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Reza Rezvani 920e8547af feat(commands): add /plugin-audit — 8-phase validation pipeline for skills and plugins

- 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>

2026-03-17 15:38:00 +01:00

2.8 KiB

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title, description

title	description
/code-to-prd — Slash Command for AI Coding Agents	Reverse-engineer a frontend codebase into a PRD. Usage: /code-to-prd [path]. Slash command for Claude Code, Codex CLI, Gemini CLI.

/code-to-prd

:material-console: Slash Command :material-github: Source

Reverse-engineer a frontend codebase into a complete Product Requirements Document.

Usage

/code-to-prd                    # Analyze current project
/code-to-prd ./src              # Analyze specific directory
/code-to-prd /path/to/project   # Analyze external project

What It Does

Scan — Run codebase_analyzer.py to detect framework, routes, APIs, enums, and project structure
Scaffold — Run prd_scaffolder.py to create prd/ directory with README.md, per-page stubs, and appendix files
Analyze — Walk through each page following the Phase 2 workflow: fields, interactions, API dependencies, page relationships
Generate — Produce the final PRD with all pages, enum dictionary, API inventory, and page relationship map

Steps

Step 1: Analyze

Determine the project path (default: current directory). Run the frontend analyzer:

python3 {skill_path}/scripts/codebase_analyzer.py {project_path} -o .code-to-prd-analysis.json

Display a summary of findings: framework, page count, API count, enum count.

Step 2: Scaffold

Generate the PRD directory skeleton:

python3 {skill_path}/scripts/prd_scaffolder.py .code-to-prd-analysis.json -o prd/

Step 3: Fill

For each page in the inventory, follow the SKILL.md Phase 2 workflow:

Read the page's component files
Document fields, interactions, API dependencies, page relationships
Fill in the corresponding prd/pages/ stub

Work in batches of 3-5 pages for large projects (>15 pages). Ask the user to confirm after each batch.

Step 4: Finalize

Complete the appendix files:

prd/appendix/enum-dictionary.md — all enums and status codes found
prd/appendix/api-inventory.md — consolidated API reference
prd/appendix/page-relationships.md — navigation and data coupling map

Clean up the temporary analysis file:

rm .code-to-prd-analysis.json

Output

A prd/ directory containing:

README.md — system overview, module map, page inventory
pages/*.md — one file per page with fields, interactions, APIs
appendix/*.md — enum dictionary, API inventory, page relationships

Skill Reference

product-team/code-to-prd/SKILL.md
product-team/code-to-prd/scripts/codebase_analyzer.py
product-team/code-to-prd/scripts/prd_scaffolder.py
product-team/code-to-prd/references/prd-quality-checklist.md

2.8 KiB Raw Blame History