jpskill.com
🛠️ 開発・MCP コミュニティ 🔴 エンジニア向け 👤 エンジニア・AI開発者

🛠️ Faf Expert

faf-expert

AIの文脈情報を扱う基盤フォーマット

⏱ MCPサーバー実装 1日 → 2時間

📺 まず動画で見る(YouTube)

▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗

※ jpskill.com 編集部が参考用に選んだ動画です。動画の内容と Skill の挙動は厳密には一致しないことがあります。

📜 元の英語説明(参考)

Advanced .faf (Foundational AI-context Format) specialist. IANA-registered format, MCP server config, championship scoring, bi-directional sync.

🇯🇵 日本人クリエイター向け解説

一言でいうと

AIの文脈情報を扱う基盤フォーマット

※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。

⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。

🎯 このSkillでできること

下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。

📦 インストール方法 (3ステップ)

  1. 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
  2. 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
  3. 3. 展開してできたフォルダを、ホームフォルダの .claude/skills/ に置く
    • · macOS / Linux: ~/.claude/skills/
    • · Windows: %USERPROFILE%\.claude\skills\

Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。

詳しい使い方ガイドを見る →
最終更新
2026-05-17
取得日時
2026-05-17
同梱ファイル
1

💬 こう話しかけるだけ — サンプルプロンプト

  • Faf Expert を使って、最小構成のサンプルコードを示して
  • Faf Expert の主な使い方と注意点を教えて
  • Faf Expert を既存プロジェクトに組み込む方法を教えて

これをClaude Code に貼るだけで、このSkillが自動発動します。

📖 Claude が読む原文 SKILL.md(中身を展開)

この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。

FAF Expert - Advanced AI Context Architecture

Master the IANA-registered format that makes AI understand your projects.

Transform any codebase into an AI-intelligent project with persistent context that survives across sessions, tools, and AI platforms. Expert-level control over the foundational layer that powers modern AI development workflows.

When to Use This Skill

Use FAF Expert when you need:

Scenario What FAF Expert Provides
Complex project setup Expert configuration of .faf files and MCP servers
Championship scoring Achieve 85%+ AI-readiness scores for production projects
Multi-AI workflows Universal context that works across Claude, Cursor, Gemini, Windsurf
Legacy codebase revival Transform archaeology into AI-readable project DNA
Team collaboration Standardized context format for consistent AI assistance
Enterprise deployment Professional MCP server configuration and management

Real-World Examples

Example 1: Legacy Enterprise Java System

# Achieved: 92% Gold tier with FAF Expert
project:
  name: enterprise-payment-api
  goal: Mission-critical payment processing system

stack:
  backend: java-spring
  database: oracle
  runtime: java-11
  deployment: kubernetes

human_context:
  where: AWS EKS production cluster
  when: Legacy system from 2018, modernizing 2026
  how: Spring Boot 2.7, Oracle 19c, Docker containerization

Example 2: Modern React Dashboard

# Achieved: 97% Gold tier performance
project:
  name: analytics-dashboard
  goal: Real-time analytics for SaaS platform

stack:
  frontend: react-18
  css_framework: tailwind
  state: zustand
  build: vite
  testing: vitest
  deployment: vercel

Core Capabilities

🏆 Championship Scoring System

  • Gold Tier (95%+): Production-ready AI context
  • Silver Tier (85%+): Professional development standard
  • Bronze Tier (70%+): Solid foundation for AI assistance

🔧 MCP Server Configuration

Expert setup of claude-faf-mcp with 33 tools:

{
  "mcpServers": {
    "faf": {
      "command": "npx",
      "args": ["-y", "claude-faf-mcp@latest"]
    }
  }
}

🔄 Bi-Directional Sync

Keep context synchronized across platforms:

  • .fafCLAUDE.md
  • .faf.cursorrules
  • .fafGEMINI.md
  • .fafAGENTS.md

📊 Mk4 Architecture Framework

33-slot IANA format for comprehensive project context:

  • Project identity and goals
  • Technical stack detection
  • Human context (who/what/why/where/when/how)
  • Architecture patterns
  • Deployment configuration

Getting Started

Quick Installation

# Install FAF CLI
npm install -g faf-cli

# Initialize your project
faf init

# Score AI-readiness
faf score --details

# Set up MCP server
faf mcp install

Expert Commands

# Advanced scoring with breakdown
faf score --championship --verbose

# Multi-platform sync
faf bi-sync --target all

# Validate format compliance
faf validate --strict

# Enhanced AI optimization
faf enhance --model claude --focus completeness

Success Metrics

Real Performance Data:

  • 52k+ downloads across FAF ecosystem
  • 800+ comprehensive tests (CLI + MCP)
  • IANA-registered format (application/vnd.faf+yaml)
  • 153+ validated formats supported
  • Championship-grade performance (<50ms execution)

Platform Compatibility

Supported AI Tools

  • Claude Code - Native MCP integration
  • Cursor - .cursorrules sync
  • Gemini CLI - GEMINI.md sync
  • Windsurf - .windsurfrules support
  • Universal - Works with any AI that reads YAML

MCP Servers Available

  • claude-faf-mcp - 33 tools, 391 tests
  • grok-faf-mcp - xAI/Grok optimized
  • rust-faf-mcp - Native performance (4.3MB binary)
  • gemini-faf-mcp - Google Gemini integration

Advanced Patterns

Enterprise Configuration

faf_version: "3.0"
project:
  name: enterprise-platform
  tier: production

human_context:
  team_size: 50+
  compliance: SOC2, HIPAA
  deployment: multi-region

stack:
  architecture: microservices
  orchestration: kubernetes
  monitoring: datadog
  security: vault

Legacy System Revival

# Transform 10-year-old codebase to AI-ready
project:
  archaeology: true
  modernization_target: 2026

stack:
  legacy: php-5.6
  migration_path: laravel-11
  database_upgrade: mysql-8

Expert Resources

  • Documentation: https://faf.one
  • MCP Registry: Official Anthropic steward
  • CLI Reference: faf --help
  • Community: Discord server with 1000+ developers
  • Enterprise: Professional support available

When to Use faf-wizard Instead

Use faf-wizard for:

  • ✅ Quick project setup
  • ✅ One-click generation
  • ✅ Beginner-friendly workflow
  • ✅ Automated stack detection

Use faf-expert for:

  • 🎯 Fine-tuned configuration
  • 🎯 Championship scoring optimization
  • 🎯 Multi-platform sync management
  • 🎯 Enterprise deployment patterns
  • 🎯 Advanced MCP server setup

Master the format that makes AI understand your projects. FAF Expert - for when you need championship-grade AI context architecture.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.