Architect AI Context for Codebases
Expert-level configuration of FAF (IANA-registered YAML format) to create AI-readable project context with championship scoring system (Gold 95%+, Silver 85%+
Why it matters
Transform any codebase into an AI-intelligent project with persistent, universal context that survives across sessions, tools, and AI platforms.
Outcomes
What it gets done
Configure .faf files and MCP servers for complex projects.
Achieve 85%+ AI-readiness scores for production projects.
Enable universal context across Claude, Cursor, Gemini, and Windsurf.
Revive legacy codebases into AI-readable project DNA.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-faf-expert | bash Capabilities
What this skill does
Runs build pipelines, tests, and deploys to environments.
Chunks, embeds, and indexes documents for semantic retrieval.
Analyzes code for bugs, style issues, and improvements.
Writes source code or scripts from a description.
Creates unit, integration, or end-to-end test cases.
Overview
FAF Expert - Advanced AI Context Architecture
What it does
A skill for configuring FAF (IANA-registered YAML format) files and MCP servers to create AI-readable project context with championship-tier scoring standards.
How it connects
Use when you need expert-level FAF configuration, multi-platform context sync, MCP server setup, or championship scoring optimization for complex codebases and enterprise deployments.
Source README
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
๐ Championship Scoring System
- Gold Tier (95%+): Production-ready AI context
- Silver Tier (85%+): Professional development standard
- Bronze Tier (70%+): Solid foundation for AI assistance
๐ง MCP Server Configuration
Expert setup of claude-faf-mcp with 33 tools:
{
"mcpServers": {
"faf": {
"command": "npx",
"args": ["-y", "claude-faf-mcp@latest"]
}
}
}
๐ Bi-Directional Sync
Keep context synchronized across platforms:
.fafโCLAUDE.md.fafโ.cursorrules.fafโGEMINI.md.fafโAGENTS.md
๐ 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
- โ Claude Code - Native MCP integration
- โ Cursor - .cursorrules sync
- โ Gemini CLI - GEMINI.md sync
- โ Windsurf - .windsurfrules support
- โ Universal - Works with any AI that reads YAML
MCP Servers Available
claude-faf-mcp- 33 tools, 391 testsgrok-faf-mcp- xAI/Grok optimizedrust-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:
- โ Quick project setup
- โ One-click generation
- โ Beginner-friendly workflow
- โ Automated stack detection
Use faf-expert for:
- ๐ฏ Fine-tuned configuration
- ๐ฏ Championship scoring optimization
- ๐ฏ Multi-platform sync management
- ๐ฏ Enterprise deployment patterns
- ๐ฏ 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.
Discussion
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