One .faf file → AGENTS.md · CLAUDE.md · GEMINI.md · .cursorrules,
detected from your real stack, scored, and versioned with your code. No drift. No re-explaining.
· across npm, PyPI and crates.io · IANA-registered · Anthropic-merged (#2759)
⭐ Bookmarks it for you, helps other devs find it too.
FAF defines. AGENTS.md instructs. AI codes.
project/
├── package.json ← npm reads this
├── project.faf ← AI reads this
├── README.md ← humans read this
└── src/
Every building requires a foundation. FAF is AI's foundational layer.
You have a
package.json. AI needs you to add aproject.faf. Done.
Git-Native. project.faf versions with your code — every clone, every fork, every checkout gets full AI context.
No setup, no drift, no re-explaining.
bunx faf auto # Bun — zero install, fastest path
npx faf auto # npm — works everywhere
pnpm dlx faf auto # pnpm — zero install
brew install wolfe-jam/faf/faf-cli && faf auto # Homebrew (auto-taps)
fafwith no arguments shows your project's score;faf autodetects and fills.
# ANY GitHub repo — one shallow clone, no install, 2 seconds
bunx faf-cli git https://github.com/facebook/react
# Your own project
bunx faf-cli init # Create .faf
bunx faf-cli auto # Fill every tech slot from the repo, then score
bunx faf-cli go # Interactive interview to gold codeRun faf with no arguments:
faf-cli dogfoods itself — project.faf is source DNA; CLAUDE.md and GEMINI.md are authored from it via
faf. AGENTS.md is the BETTER ops briefing (hand-kept for agents;faf export --agentsstill authors AGENTS.md for other repos).
| Command | What it does |
|---|---|
faf init |
Create project.faf from your local project |
faf git <url> |
Instant .faf from any GitHub repo (a shallow clone) |
faf auto |
Detect stack, fill every slot it can, score |
faf go |
Guided interview to fill the human-only slots |
faf score |
Check AI-readiness (0–100%) |
faf export |
Author AGENTS.md, CLAUDE.md, GEMINI.md, .cursorrules |
faf sync |
.faf → CLAUDE.md (pull: Trophy-gated backfill) |
faf memory |
.fafm soul ops — convert Claude memory, etch, recall, ls, show |
faf diff / log |
Semantic context diff + score timeline across git history |
faf hooks --install |
Pre-commit guard against context regression |
faf compile / decompile |
.faf → .fafb v2 sealed brick; decompile shows sections as JSON |
faf check |
Validate a .faf file |
faf recover |
Rebuild .faf from an existing CLAUDE.md / AGENTS.md |
faf show |
Render project.faf to a browsable HTML page |
faf formats |
List supported stacks and formats |
Run faf --help for the full command set and options.
Portable agent memory in the IANA-registered .fafm format. Same INTEROP as claude-fafm-sdk 1.0.
# Claude Code memory dir → soul.fafm
faf memory convert ~/.claude/projects/.../memory -o soul.fafm
faf memory ls # ranked facts
faf memory recall "your query" # deterministic filter + rank
faf memory etch "a durable fact" --id my-fact
faf memory showfaf-cli reads SvelteKit 3 projects: SvelteKit, its adapter and its hosting, from vite.config and devDependencies.
npx faf-cli@latest auto # in a SvelteKit 3 appfrontend: SvelteKit · backend: SvelteKit · hosting: Cloudflare · build: Vite 8
- SvelteKit 3 moved its config into
vite.config. faf-cli reads the adapter there (and still insvelte.config.jsfor SvelteKit 2), and finds the kit in devDependencies, where SvelteKit apps list it. - The build slot names Vite's major version:
Vite 8.
From nothing to listed in one command: faf card init asks seven questions and writes your agent.fafa, your AI Catalog entry and your ARD entry.
The same always-33 engine, plus cards.
npx faf-cli@latest card init # seven questions → agent.fafa → A2A card, AI Catalog, ARD| You have | faf cards gives you |
Rung |
|---|---|---|
agent.fafa |
A2A card, MCP Server Card, registry, AI Catalog, ARD (as its endpoints allow) | BETTER |
+ project.faf |
the same cards, with FAF context | BEST |
- Scripts ask nothing: every answer is a flag (
--name,--domain,--urlor--package,--skill,--example,--set-version). - Help says what each command touches, with a "What touches what" footer and docs.faf.one/side-effects.
One engine, one number: faf-cli v8 scores with the always-33 kernel — the same score faf-kernel, faf-rust-sdk and rust-faf-mcp give.
- The always-33 engine. Every
.fafis scored against all 33 Mk4 slots by one Rust kernel (faf-scoring-kernel3.0.0, WASM). Verified identical to the reference always-33 scorer on theproject.fafof all 80 FAF repos. - Your 21 slots, and the 12 enterprise slots in view. faf-cli fills the 21 base slots. The 12 enterprise slots — infra, app, ops — are marked
slotignoredunless your app-type uses them, andfaf scoreshows all 33. - Upgrading from 7.x: a
.fafwithout the 12 enterprise markers now scores against 33, so its number can drop (21 filled = 64%). Runfaf auto— it writes the markers and your score returns.
✪ TROPHY 100% 21/21 slots — project.faf
● project.name
…
● stack.cicd
— stack.monorepo_tool: slotignored
…
— monorepo.remote_cache: slotignored
Recent sprint
- ✪ 8.1.1 SvelteKit 3 read in full — kit, adapter, Vite 8
- ✪ 8.1.0 The Always33+ Edition — from nothing to listed in one command
- ✪ 8.0.1 pnpm installs work — faf-scoring-kernel from npm
- ✪ 8.0.0 The Always33 Edition — one engine, one number
- 🧱 7.16.2
faf compileemits FAFb wire v2 — same bytes as the faf-fafb golden - 🧭 7.16.1
fafback in step withfaf-cli - 🧭 7.16.0 The Discoverable Edition
- 🎴 7.15.0 The Pack Edition
- 🛂 7.14.0 The Passport Edition
- 🛡️ 7.13.1 security: detection reads stay inside the project
- 🤝 7.13.0 The Co-Author Edition
- 🧩 7.12.0 The Open Renderers Edition
- 🖥️ 7.11.0 The VS Code Edition
- 📚 7.10.0 The Full-Facts Edition
- 🌱 7.9.0 The Git-Flow Edition
- 🎬 7.8.0 The Projector Edition
- 🐦 7.7.0 The Swift Edition
- 💎 7.6.0 The Ruby Edition
- ☕ 7.5.1 The JVM Edition
Your own rules for the AI — "use full words in identifiers," "use bun, not npm" — go in project.faf under ai_instructions.warnings. They land at the top of every AGENTS.md faf writes, verbatim and non-destructive.
→ How to add custom rules · docs.faf.one
✪ Trophy 100% — all or nothing. From v6.6.0 onward, faf-cli recommends only Trophy. 100% on the FCL is what makes the layers above (MD instructions, Agents, AI tooling) work — sub-Trophy leaves gaps that AI guesses on. Sub-Trophy tiers (including Bronze 85) remain on the ladder as honest interim states — they are not deleted; we just no longer aim for 85 as the goal.
| Tier | Score | Status |
|---|---|---|
| ✪ Trophy | 100% | AI never has to guess — target |
| ★ Gold | 99%+ | 1 slot from Trophy |
| ◆ Silver | 95%+ | Close — keep going |
| ◇ Bronze | 85%+ | On the ladder (was the old recommend-min; not the target) |
| ● Green | 70%+ | Interim — keep going |
| ● Yellow | 55%+ | AI flipping coins |
| ○ Red | <55% | AI working blind |
| ♡ White | 0% | No context at all |
Always-33. The score is filled ÷ active across all 33 Mk4 slots. Your app-type decides which slots are active; the rest are slotignored and stay visible. Same file, same engine, same number — see docs/SCORING.md.
One score, three glyphs: ✪ work (CLI · docs · receipts) · 🏆 social (X · blogs) · Trophy Mark PNG (brand). Source of truth: src/core/tiers.ts.
sync: .faf ──── 8ms ───→ CLAUDE.md (pull: Trophy-gated backfill)
tri-sync: .faf ──── 8ms ───→ CLAUDE.md + Claude Code's MEMORY.md (Pro: faf's block only; Claude's notes kept)
The full manual lives at docs.faf.one — facts for devs, faf-cli first. One-page overview: faf-cli.vercel.app.
- Getting started — install · run · use
- Cards — one
.fafa→ A2A · Server Card · registry · AI Catalog · ARD - Custom rules — pin instructions your AI must follow
For a specific agent: Grok, xAI & Cursor 👀 · Claude Code 👀 · Bun 👀
Pivotal releases — full history in CHANGELOG.md:
- v8.0 — Always33 — one engine, one number: the same score everywhere.
- v7.1 — AGENTS.md —
faf export --agentsauthors a complete, non-destructiveAGENTS.md. - v7.0 — GIT — context goes git-native:
faf diff/log/hooks. - v6.16 — Know Your Stack — every emitted file labels your stack identically.
- v6.15 — Copilot —
faf export --copilotwrites the file GitHub Copilot reads. - v6.14 — Loop —
faf loopdrives any repo to ✪ 100% or the honest human wall. - v6.7 — HTML —
faf showrenders a.fafto a browsable page. (FAF defines. AGENTS.md instructs. AI codes. HTML shows.) - v6.6 — Trophy — 100% or nothing.
- v6.0 — Bun — ground-up rewrite; single portable binary, four platforms.
Bun's single-file compiler produces standalone binaries — no runtime needed.
bun run compile # Current platform
bun run compile:all # darwin-arm64, darwin-x64, linux-x64, windows-x64Ship faf as a single binary for CI/CD, Docker, or air-gapped environments.
src/
├── cli.ts ← Entry point (Commander registrations)
├── commands/ ← one file per faf subcommand
├── core/ ← Types, slots (Mk4), tiers, scorer, schema
├── detect/ ← Framework detection, stack scanner
├── interop/ ← YAML I/O, CLAUDE.md, AGENTS.md, GEMINI.md
├── ui/ ← Colors (#00D4D4), display
└── wasm/ ← faf-scoring-kernel wrapper (Rust → WASM)
Toolchain: Bun (test, build, compile) · TypeScript (strict) · WASM (scoring kernel)
Robust. Reliable. Next-level WJTTC tested. — The Foundation Edition.
bun test # extensive WJTTC + e2e suite- WJTTC Build Resilience — regression classes locked.
- WJTTC Kernel Stress — WASM kernel boundary tests.
- e2e lifecycle — commands in sequence.
Test reports in reports/.
- GitHub Discussions — Questions, ideas, community
- Email: team@faf.one
If faf-cli has been useful, consider starring the repo — it helps others find it.
If you use faf-cli or the .faf / .fafm / .fafa formats in research or production, please cite the format papers:
Wolfe, J. (2025). Format-Driven AI Context Architecture: The .faf Standard for Persistent Project Understanding. Zenodo. https://doi.org/10.5281/zenodo.18251362
Wolfe, J. (2026). Permanent Memory and Instant Recall: The .fafm Standard for Multi-Profile AI Agent Memory. Zenodo. https://doi.org/10.5281/zenodo.20348942
Wolfe, J. (2026). Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era. Zenodo. https://doi.org/10.5281/zenodo.21951641
@article{wolfe2025faf,
title = {Format-Driven AI Context Architecture: The .faf Standard for Persistent Project Understanding},
author = {Wolfe, James},
year = {2025},
month = {nov},
publisher = {Zenodo},
doi = {10.5281/zenodo.18251362},
url = {https://doi.org/10.5281/zenodo.18251362}
}
@article{wolfe2026fafm,
title = {Permanent Memory and Instant Recall: The .fafm Standard for Multi-Profile AI Agent Memory},
author = {Wolfe, James},
year = {2026},
month = {may},
publisher = {Zenodo},
doi = {10.5281/zenodo.20348942},
url = {https://doi.org/10.5281/zenodo.20348942}
}
@article{wolfe2026fafa,
title = {Why Agents Need a Passport: .fafa — Portable Identity for the Agentic Era},
author = {Wolfe, James},
year = {2026},
month = {aug},
publisher = {Zenodo},
doi = {10.5281/zenodo.21951641},
url = {https://doi.org/10.5281/zenodo.21951641}
}MIT — Free and open source
IANA-registered: application/vnd.faf+yaml (Context Layer) · application/vnd.fafm+yaml (Memory Layer) · application/vnd.fafa+yaml (Agent Layer)
format | driven 🏎️⚡️ wolfejam.dev · faf.one/cli
