tomevault-io 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."
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