
faf-docs
by Wolfe-Jam
Championship-Grade Claude Code Skills for project.faf Files
SKILL.md
name: faf-docs description: Access FAF documentation, guides, and resources. Answers questions about The Reading Order, IANA registration, Podium scoring, format specification, and best practices. Use when user asks "how does FAF work", "show me docs", "explain The Reading Order", or needs reference information. allowed-tools: WebFetch, Read
FAF Docs - Documentation & Resources
Purpose
Provide quick access to FAF documentation, guides, specifications, and learning resources. Answers common questions and explains FAF concepts without requiring users to leave their workflow.
The Goal: Instant answers. Zero context switching. Championship-grade documentation.
When to Use
This skill activates automatically when the user:
- Asks "How does FAF work?"
- Says "Show me the docs"
- Says "Explain The Reading Order"
- Asks "What is IANA registration?"
- Says "How do I use Podium scoring?"
- Asks "Where are the FAF docs?"
- Needs format specification reference
- Wants to learn best practices
- Asks "What's the difference between project.faf and CLAUDE.md?"
Trigger Words: docs, documentation, guide, explain, how does, what is, show me, reference, spec, help
How It Works
Step 1: Identify Question Type
Determine what the user needs:
Conceptual:
- "What is FAF?" → Use faf-teacher skill for comprehensive overview
- "What is The Reading Order?" → Explain reading sequence
- "What is Podium scoring?" → Explain 0-100% AI-readiness system
Reference:
- "Show format spec" → Provide YAML structure reference
- "List all commands" → Show faf-cli command reference
- "Show IANA details" → Explain registration
Practical:
- "How do I improve my score?" → Use faf-enhance skill
- "How do I sync files?" → Use faf-sync skill
- "How do I validate?" → Use faf-validate skill
Step 2: Provide Answer
For conceptual questions:
- Explain concept clearly
- Provide examples
- Link to related skills
- Suggest next steps
For reference questions:
- Show relevant specification
- Provide code examples
- Link to full documentation
For practical questions:
- Activate appropriate skill
- Guide through process
- Show command examples
Step 3: Offer Additional Resources
After answering, provide:
- Related documentation links
- Relevant skills to use
- Next learning steps
- Official resources
Key Concepts
The Reading Order
What it is: Optimal sequence for AI to read project files for complete context understanding.
The Order:
1. project.faf → Project DNA (architecture, purpose, stack)
2. CLAUDE.md → Workflow instructions (how to work here)
3. README.md → User documentation (how humans use it)
4. package.json → Dependencies (what it needs)
5. Config files → Build settings (how to compile)
6. Code → Implementation (what it does)
Why this order:
- Architecture BEFORE implementation
- Context BEFORE code
- Complete picture BEFORE details
- Persistent BEFORE temporary
Analogy: Like reading blueprints before examining bricks. You understand WHAT you're building before diving into HOW it's built.
IANA Registration
What it is: FAF is officially registered with the Internet Assigned Numbers Authority as a recognized media type.
Details:
- Media Type:
application/vnd.faf+yaml - Registration Date: October 31, 2025
- Authority: IANA (same authority that registered PDF, JSON, XML)
- Status: Official Internet standard
Why it matters:
- Universal recognition (browsers, email clients, APIs)
- Format authority (not a tool, foundational infrastructure)
- Same level as PDF, JSON, XML
- Professional legitimacy
How to verify:
# In your project.faf
metadata:
iana_media_type: application/vnd.faf+yaml
Podium Scoring
What it is: AI-readiness measurement system (0-100%) showing how well AI can understand your project.
Tiers:
- 🏆 Trophy (85-100%) - Elite AI-readiness
- 🥇 Gold (70-84%) - Excellent AI-readiness
- 🥈 Silver (55-69%) - Good AI-readiness
- 🥉 Bronze (40-54%) - Basic AI-readiness
- 🟢 Green (35-39%) - Minimal AI-readiness
- 🟡 Yellow (20-34%) - Very limited AI-readiness
- 🔴 Red (10-19%) - Critical gaps
- 🤍 White (<10%) - No effective AI-readiness
What gets scored:
- Project purpose clarity
- Architecture documentation
- Stack information completeness
- Testing approach detail
- Dependencies specification
- Format compliance
How to check:
faf score
How to improve:
faf enhance
Context-Mirroring
What it is: Bidirectional synchronization between project.faf and CLAUDE.md in <10ms (achieved: 8ms).
How it works:
- Changes in project.faf → auto-sync to CLAUDE.md
- Changes in CLAUDE.md → auto-sync to project.faf
- Architecture stays consistent
- Workflow stays separate
- Zero manual work
When to use:
# After editing either file
faf bi-sync
Performance:
- Target: <10ms
- Achieved: 8ms average
- F1-grade engineering
- Championship performance
Common Questions
"What's the difference between project.faf and CLAUDE.md?"
project.faf (Project DNA):
- Purpose: Architecture and project structure
- Audience: AI understanding "WHAT it is"
- Changes: Rarely (only when architecture changes)
- Format: YAML (machine-readable, human-friendly)
- Contains: Stack, framework, dependencies, testing, architecture
CLAUDE.md (Workflow Instructions):
- Purpose: How to work in this codebase
- Audience: AI understanding "HOW to work here"
- Changes: Frequently (as workflow evolves)
- Format: Markdown (human-readable)
- Contains: Git protocol, code style, development commands, team conventions
Together: Complete AI context (architecture + workflow)
"Do I need both files?"
Minimum:
- project.faf alone = Good (architecture context)
Recommended:
- project.faf + CLAUDE.md = Excellent (architecture + workflow)
Best:
- project.faf + CLAUDE.md + bi-sync = Elite (perfect sync)
"How often do I update project.faf?"
Update when:
- ✅ Major framework upgrade (Next.js 13 → 14)
- ✅ Runtime version change (Node 18 → 20)
- ✅ Architecture shift (REST → GraphQL)
- ✅ Testing framework change (Jest → Vitest)
- ✅ New major dependency added
Don't update for:
- ❌ New features (code shows that)
- ❌ Bug fixes (code shows that)
- ❌ Dependency version bumps (package.json shows that)
Think: "Would a new developer need to know this?" If yes, update project.faf.
"Can I use FAF with [other AI tool]?"
Yes. FAF works with ALL AI tools:
- ✅ Claude Code (native support)
- ✅ Claude Desktop
- ✅ Cursor
- ✅ Gemini CLI
- ✅ OpenAI Codex CLI
- ✅ Windsurf
- ✅ Warp
- ✅ ANY AI tool (it's just text)
Why: FAF is a text file in YAML format. Any AI can read YAML. No special integration required. Universal by design.
"What if my team doesn't use FAF?"
You can still benefit personally:
- Create project.faf for YOUR understanding
- Drop it into YOUR AI conversations
- Save 10-30 min per session
- Share with team when they see the value
Team adoption is automatic:
- Commit project.faf to repository
- Team members' AI tools read it automatically
- Everyone saves time
- No coordination required
Format Specification Reference
Minimal Valid Format
name: project-name
purpose: What this project does
version: 1.0.0
metadata:
format_version: 3.0.0
iana_media_type: application/vnd.faf+yaml
architecture:
type: web-app | library | api | cli
language: TypeScript | Python | etc.
Comprehensive Format
name: project-name
purpose: Detailed description of what this project does and why it exists
version: 1.0.0
metadata:
format_version: 3.0.0
iana_media_type: application/vnd.faf+yaml
created: 2025-11-02
last_updated: 2025-11-02
architecture:
type: web-app
language: TypeScript
framework: Next.js 14
pattern: Server-side rendering with App Router
description: |
Comprehensive architecture description explaining
the structure, patterns, and design decisions.
stack:
runtime: Node.js 18+
framework: Next.js 14 (App Router)
dependencies:
- react: "^18.2.0"
- typescript: "^5.3.3"
styling: Tailwind CSS
database: PostgreSQL 15 (Prisma ORM)
testing:
framework: Jest + React Testing Library
approach: |
- Unit tests for utilities
- Component tests for UI
- Integration tests for API routes
coverage: 78% overall, 90%+ critical paths
commands:
test: npm test
coverage: npm run test:coverage
build:
tool: Next.js (Turbopack)
commands:
dev: npm run dev
build: npm run build
start: npm start
deployment:
platform: Vercel
environment: Node.js 18
env_variables:
- DATABASE_URL
- JWT_SECRET
architecture_decisions:
- decision: "Chose App Router over Pages Router"
rationale: "Better performance, simpler data fetching"
- decision: "TypeScript strict mode"
rationale: "Catch errors at compile time"
performance:
- React Server Components for data fetching
- Image optimization with next/image
- Code splitting by route (automatic)
Command Reference
faf-cli Commands
Essential:
faf init # Initialize project.faf
faf score # Check AI-readiness (0-100%)
faf enhance # Improve project.faf
faf validate # Check format compliance
faf bi-sync # Sync project.faf ↔ CLAUDE.md
faf migrate # Upgrade to latest format
Discovery:
faf auto # Auto-detect and generate everything
faf doctor # Health check for project
faf verify # Verify installation
Analysis:
faf formats # Show detected formats
faf stacks # Show detected tech stacks
faf vibe # AI-powered project analysis
Utility:
faf show # Display current project.faf
faf version # Show faf-cli version
faf help # Show help
Official Resources
Website:
- https://faf.one - Main website
- https://faf.one/docs - Full documentation
GitHub:
- faf-cli: https://github.com/Wolfe-Jam/faf-cli
- claude-faf-mcp: https://github.com/Wolfe-Jam/claude-faf-mcp
npm:
- faf-cli: https://npmjs.com/package/faf-cli
- claude-faf-mcp: https://npmjs.com/package/claude-faf-mcp
Homebrew:
brew install wolfe-jam/faf/faf-cli
Related Skills
For specific tasks, use:
- faf-teacher - Comprehensive FAF overview
- faf-init - Create project.faf
- faf-score - Measure AI-readiness
- faf-enhance - Improve quality
- faf-sync - Bidirectional sync
- faf-validate - Format compliance
- faf-migrate - Version upgrade
Examples
Example 1: "What is The Reading Order?"
Answer:
"The Reading Order is the optimal sequence for AI to read your project files:
1. project.faf → Project DNA (WHAT it is)
2. CLAUDE.md → Workflow (HOW to work)
3. README.md → Docs (HOW humans use it)
4. package.json → Dependencies
5. Config files → Build settings
6. Code → Implementation
Why this order matters:
- Architecture BEFORE code = Better understanding
- Context BEFORE details = Fewer questions
- Complete picture FIRST = Correct suggestions
Analogy: Reading blueprints before examining bricks. You understand the building plan before diving into construction details.
Result: AI has complete context in <1 second vs 10-30 minutes of questions."
Example 2: "Show me the format spec"
Answer:
"Here's the IANA-registered format specification:
Minimal valid project.faf:
name: project-name
purpose: What it does
version: 1.0.0
metadata:
format_version: 3.0.0
iana_media_type: application/vnd.faf+yaml
architecture:
type: web-app
language: TypeScript
Create yours:
faf init # Auto-generates valid format
Full spec: https://faf.one/docs/format"
Example 3: "How do I improve my score?"
Answer:
"Use the faf-enhance skill for guided improvement:
faf enhance
This will:
- Analyze your current project.faf
- Identify missing or weak sections
- Ask questions to gather information
- Add improvements incrementally
- Re-score after each change
- Guide you to your target tier
Typical progression:
- Start: 58% (Silver)
- +6% add testing docs → 64%
- +4% add API docs → 68%
- +2% add architecture → 70% (Gold) ✨
Want me to activate the faf-enhance skill now?"
Key Principles
Documentation as Code:
- Docs live with code
- Examples are runnable
- Specifications are tested
- Truth in one place
NO BS ZONE:
- Only verified claims
- Real examples
- Honest limitations
- Professional tone
Format-Driven:
- IANA-registered standard
- Not a tool, infrastructure
- Universal compatibility
- Long-term stability
Success Metrics
When this skill succeeds, users should:
- Understand FAF concepts clearly
- Know where to find detailed docs
- Have answers to their specific questions
- Know which skill to use for their task
- Feel confident using FAF
- Trust the documentation
Generated by FAF Skill: faf-docs v1.0.0 Documentation Edition: Instant Answers "The Reading Order. IANA registration. Championship docs."
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
1ヶ月以内に更新
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon


