name: faf-context description: Generate AI-context DNA for ANY project - new, legacy, famous, or forgotten. Creates .faf files that eliminate "explain your codebase again" across ALL AI tools. IANA-registered format (application/vnd.faf+yaml), 21,000+ npm downloads, Anthropic MCP ecosystem. Use when Claude asks "what does this project do?", when context drifts between sessions, or when onboarding AI to any codebase. Works with Claude, Cursor, Gemini CLI, WARP, Windsurf.

FAF Context - AI-Context DNA for Any Project

Generate .faf files that give any AI instant, persistent understanding of your project.

The Truth About AI Context

Even famous repos score 0%. React. Kubernetes. Rails. Django. None have AI-context.

Why? Because AI-context is a NEW requirement. It didn't exist until AI became a coding partner.

What exists	What it answers
README.md	"What does this do?" (for humans)
docs/	"How do I use this?" (for humans)
project.faf	"How should AI help build this?" (for AI)

Documentation tells humans how to use code. AI-context tells AI how to help you write it. They're not the same thing.

Works on ANY Project

Project State	What FAF Context Adds
Brand new	Optimized AI context from line one
Legacy codebase	AI finally understands the archaeology
Famous OSS	Even React needs this (it doesn't have one)
Side project	Stop re-explaining every session
Client handoff	Portable context for any AI tool

Workflow

Step 1: Detect Stack

Look for manifest files:

package.json → Node.js/TypeScript
Cargo.toml → Rust
pyproject.toml → Python
go.mod → Go
pom.xml → Java
Gemfile → Ruby

No manifest? Still generate - ask user for stack info.

Step 2: Mine Existing Context

Pull signals from what exists:

README.md (description, purpose)
docs/ (architecture decisions)
CLAUDE.md or .cursorrules (existing AI context - migrate it)
Package manifests (dependencies reveal intent)
Directory structure (architecture patterns)

Step 3: Generate project.faf

Create focused AI context at project root:

faf_version: "2.0"
project:
  name: my-project
  description: One-line purpose
  stack: [typescript, react]

context:
  architecture: |
    Brief system overview.
  key_files:
    - src/index.ts: Entry point
    - src/api/: API handlers
  conventions:
    - TypeScript strict mode
    - Composition over inheritance

ai_guidance:
  priorities: [correctness, simplicity, performance]
  avoid: [over-engineering, premature optimization]
  testing: Jest with >80% coverage

Keep under 200 lines. Only what changes AI behavior.

Step 4: Score and Report

Generated: project.faf (147 lines)
AI-Readiness: 87% [BRONZE] - Production ready

To reach Silver (95%):
  - Add deployment section (+5%)
  - Document API patterns (+3%)

AI-Readiness Tiers

Tier	Score	Meaning
Big Orange	105%	Michelin Star - Perfect + bonus
Trophy	100%	Perfect compliance
Gold	99%+	Exceptional
Silver	95%+	Excellent
Bronze	85%+	Production ready
Green	70%+	Solid foundation
Yellow	55%+	Needs improvement
Red	<55%	Major gaps

Target: Bronze (85%+). This is where AI collaboration becomes productive.

Stack Templates

See references/templates.md for TypeScript, Rust, Python, Go templates.

Migration

When CLAUDE.md or .cursorrules exist:

Offer to migrate to .faf (portable format)
.faf works in Cursor, Gemini, WARP - not just Claude
YAML structure parses better than freeform markdown

Validation

Generated files must:

Be valid YAML
Have faf_version and project.name
Contain NO secrets or credentials
Stay under 500 lines

Credentials

IANA Media Type: application/vnd.faf+yaml (registered Oct 31, 2025)
npm Downloads: 21,000+
Anthropic MCP: Registry #2759 (merged)
Website: https://faf.one

Resources

Format Specification - Complete .faf schema
Tier System - Scoring methodology
Stack Templates - Per-language examples

faf-context

SKILL.md