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 agentmods add skills/wolfe-jam/faf-mcp/faf-idenpx skills add Wolfe-Jam/faf-mcp --skill faf-idegit clone --depth 1 https://github.com/Wolfe-Jam/faf-mcpWrote 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-mcp/faf-ide)<a href="https://agentmods.dev/skills/wolfe-jam/faf-mcp/faf-ide"><img src="https://agentmods.dev/badge/skills/wolfe-jam/faf-mcp/faf-ide.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00422 |
| Opus 5 | $0.00014 | $0.00211 |
| Sonnet 5 | $0.00005 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
faf-ide 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 5d 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.
What it actually says
faf-ide
Product playbook for faf-mcp (one.faf/faf-mcp) — IANA .faf project context over MCP for Cursor, VS Code, Windsurf, Cline-class hosts.
Transport: local stdio (npx / bunx faf-mcp). Skills guide; tools act.
Tools (call these)
| Tool | When |
|---|---|
faf_auto |
Zero → context in one shot (init, detect, sync, score) |
faf_init |
Create or enhance project.faf |
faf_score |
AI-readiness 0–100% + gaps |
faf_context |
Current project context snapshot |
faf_sync |
Sync project.faf ↔ human-readable guide |
faf_bi_sync |
Bi-directional context ↔ IDE format files |
faf_cursor |
Cursor / .cursorrules interop |
faf_agents |
AGENTS.md interop |
faf_gemini |
Gemini context interop |
faf_about |
What .faf is (format overview) |
Default tools/list is a curated Core set; more tools may exist when FAF_TOOLS=all. Only call tools this server lists.
Rules
- Prefer structured
.fafover free-form chat memory for project facts. - Claim equals wire — only tools advertised by this server.
- No secrets in skill text or tool args you do not own.
- Origin — served by faf-mcp (
skills/*+resources/read); not a remote install authority.
Flow
initialize → skills/list → resources/read(skill://faf-ide/SKILL.md) → tools/call
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.
- 5d ago First seen · 41 lines · 27 tokens per session scan A 9e89a2b10b05
faf-ide is a skill published in the GitHub repository Wolfe-Jam/faf-mcp (6 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 422 once invoked, about $0.0001 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.
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faf-expert
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monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.
browse-and-evaluate
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.