---
name: faf-expert-59db0cec54f1
description: "Advanced .faf (Foundational AI-context Format) specialist. IANA-registered format, MCP server config, championship scoring, bi-directional sync."
category: coding
risk: safe
source: community
source_repo: Wolfe-Jam/faf-skills
source_type: community
date_added: "2026-04-07"
author: wolfejam
tags: [faf, ai-context, project-management, mcp, iana]
tools: [claude, cursor, gemini, windsurf]
---

# 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
```yaml
# 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
```yaml
# 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:
```json
{
  "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
```bash
# 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
```bash
# 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
```yaml
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
```yaml
# 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.


---
Source attribution: wolfejam
Original project: https://github.com/FrancoStino/opencode-skills-collection/blob/90fae54712f8337fc1ca522c5dd401703f11e2c7/bundled-skills/faf-expert/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Davide Ladisa

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: wolfejam
Original project: https://github.com/FrancoStino/opencode-skills-collection/blob/90fae54712f8337fc1ca522c5dd401703f11e2c7/bundled-skills/faf-expert/SKILL.md
Attribution basis: Source repository record

Fields of work: Project & Program Management, Development & Engineering, Product Management
Roles: Project Manager, ML / AI Engineer, Backend Developer, Data Engineer, Growth Product Manager
Specialties: API, Backend
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: c7706653b5e218421dc429fcfbc7b30bfa96a37785018f8b3e25ce17cfa02b1e