Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Wolfe-Jam/faf-skills --skill faf-expertgit clone --depth 1 https://github.com/Wolfe-Jam/faf-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wolfe-jam/faf-skills/faf-expert)<a href="https://agentmods.dev/skills/wolfe-jam/faf-skills/faf-expert"><img src="https://agentmods.dev/badge/skills/wolfe-jam/faf-skills/faf-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wolfe-jam/faf-skills/faf-expert"><img src="https://agentmods.dev/badge/skills/wolfe-jam/faf-skills/faf-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00098 | $0.02616 |
| Opus 5 | $0.00049 | $0.01308 |
| Sonnet 5 | $0.00020 | $0.00523 |
| Haiku 4.5 | $0.00010 | $0.00262 |
Grade A, and why
faf-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FAF Expert — Master the Format
.faf is an IANA-registered context format (application/vnd.faf+yaml) — a typed, portable file you own, readable by any AI, with no bespoke manifest and no vendor lock-in. This is the mechanic's manual: scoring internals, MCP configuration, bi-directional sync, and the full slot model.
The FAF skill family — where this fits
| Skill | What it's for | |
|---|---|---|
| faf-wizard | Done-for-you — one-click .faf for any project |
DFY |
| faf-context | The builder's quickstart — hand the AI what it needs to hit 100% ✪, fast | ← start here if you're new |
| faf-expert (you are here) | Master the format — scoring internals, MCP config, bi-sync, the full slot model | Deep |
New to FAF? Start with faf-context to reach 100%. Come here to go deep.
When to use this skill
- Configuring
.faffiles and the MCP server beyond the basics - Understanding the scoring engine and how to drive a project to high tiers
- Multi-AI workflows — one context across Claude, Cursor, Gemini, Codex, Windsurf
- Reviving a legacy codebase into AI-readable project DNA
- Standardizing context format across a team
The format
project/
├── package.json ← dependencies (npm reads this)
├── project.faf ← AI context (any AI reads this)
├── CLAUDE.md ← human docs (synced from .faf)
└── src/ ← code (guided by the context)
README.md is prose for humans · CLAUDE.md is prose for Claude · project.faf is structure for ANY AI (IANA application/vnd.faf+yaml). One source of truth; everything else is emitted from it.
Universal compatibility — one .faf, every tool
| AI platform | Emitted file | Command |
|---|---|---|
| Claude (Desktop / Code) | CLAUDE.md |
faf sync |
| OpenAI Codex / agents | AGENTS.md |
faf sync |
| Cursor | .cursorrules |
faf export --cursor |
| Google Gemini | GEMINI.md |
faf export --gemini |
| GitHub Copilot | .github/copilot-instructions.md |
faf export --copilot |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 245 lines · 98 tokens per session scan A fe5cc08bbbe9
faf-expert is a skill published in the GitHub repository Wolfe-Jam/faf-skills (4 stars, last pushed 21d ago), licensed MIT. It adds 98 tokens to every session and 2,616 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
agent-expert-creation
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…