Antigravity-awesome-skills faf-expert

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

install

source · Clone the upstream repo

git clone https://github.com/sickn33/antigravity-awesome-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/sickn33/antigravity-awesome-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/antigravity-awesome-skills-claude/skills/faf-expert" ~/.claude/skills/sickn33-antigravity-awesome-skills-faf-expert && rm -rf "$T"

manifest: plugins/antigravity-awesome-skills-claude/skills/faf-expert/SKILL.md

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:

```
.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

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.