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:

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:

• .faf ↔ CLAUDE.md
• .faf ↔ .cursorrules
• .faf ↔ GEMINI.md
• .faf ↔ AGENTS.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

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