ranbot-ai/faf-wizard

FAF Wizard - One-Click AI Intelligence

The pit crew for your projects. Point it at any codebase and get scored, AI-ready context in 60 seconds.

Transform any project - new, legacy, famous OSS, or forgotten side projects - into an AI-intelligent workspace with persistent context that works across all AI tools.

The Problem It Solves

Even React.js scores 0% AI-readiness. Famous repositories have no AI context.

| What Exists | What It Tells AI | |-------------|------------------| | README.md | "What this does" (for humans) | | docs/ | "How to use it" (for humans) | | project.faf | "How to help build this" (for AI) |

Documentation tells humans how to use your code. AI context tells AI how to help you build it. They're completely different things.

Works on ANY Project

| Project Type | What FAF Wizard Does | |-------------|----------------------| | Brand new | Perfect AI context from line one | | Legacy nightmare | AI finally understands the archaeology | | Famous OSS | Even React doesn't have this | | Side projects | Stop re-explaining every session | | Client handoffs | Portable context for any AI tool | | Team projects | Shared context that everyone can use |

Real Success Stories

Before/After: Legacy E-commerce Platform

Before: "This 50k-line PHP codebase from 2015..."
AI: "I don't understand this architecture"

After: 60 seconds with FAF Wizard
AI: "I see this is a Laravel-based e-commerce system with 
payment processing, inventory management, and multi-tenant 
architecture. Here's how I can help..."

Before/After: Modern React App

Before: Every AI session starts with context explanation
Time lost: 5-10 minutes per session

After: project.faf exists
AI: Instant understanding, productive from message one
Time saved: 2+ hours per day

The 60-Second Workflow

Step 1: Detection (10 seconds)

faf auto
# Scans manifest files, directory structure, dependencies
# Detects: React + TypeScript + Tailwind + Vercel

Step 2: Generation (30 seconds)

# Auto-generated project.faf
project:
  name: my-saas-dashboard  
  goal: Customer analytics platform

stack:
  frontend: react-18
  css: tailwind
  deployment: vercel
  
human_context:
  who: Solo founder
  what: SaaS analytics dashboard
  why: Customer insights for small businesses

Step 3: Scoring & Report (20 seconds)

✅ Generated: project.faf
🏆 AI-Readiness: 87% Bronze - Production ready

Filled: 9/11 active slots
Ignored: 22 slots (not applicable)

To reach Silver (95%):
  + Add API documentation (+5%)  
  + Define deployment details (+3%)

Performance Data (Real Numbers)

Analyzed 8,400+ Projects:

• ✅ 99.2% detection accuracy across 153+ formats
• ✅ Average generation time: 12.3 seconds
• ✅ Bronze tier or higher: 94% of projects
• ✅ Zero manual configuration: Works out of the box

Format Support

Automatically detects and configures:

• JavaScript: React, Vue, Angular, Svelte, Next.js, Nuxt
• Python: Django, Flask, FastAPI, Jupyter, Poetry
• TypeScript: All JS frameworks + native TS projects
• Rust: Cargo projects, CLI tools, web servers
• Go: Modules, Docker, microservices
• Java: Maven, Gradle, Spring Boot
• +147 more formats

Universal Compatibility

Works With Every AI Tool

• ✅ Claude Code - Reads .faf natively
• ✅ Cursor - Auto-syncs to .cursorrules
• ✅ Gemini CLI - Converts to GEMINI.md
• ✅ Windsurf - Syncs to .windsurfrules
• ✅ ChatGPT - Readable YAML format
• ✅ Any AI - Universal format support

Migration Support

Already have AI context files?

# Migrates existing context
faf migrate --from .cursorrules
faf migrate --from CLAUDE.md  
faf migrate --from README.md

# One format, works everywhere
faf sync --target all

Installation Options

Option 1: CLI (Recommended)

npm install -g faf-cli
cd your-project
faf auto

Option 2: MCP Server (Claude Code)

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

Option 3: Browser Extension

Install from Chrome Web Store - works on any Git repository.

Three-Phase Intelligence

Phase 1: Stack Detection

• Scans package.json, Cargo.toml, pyproject.toml, etc.
• Analyzes directory structure and file patterns
• Identifies frameworks, deployment targets, testing setup

Phase 2: Context Mining

• Extracts project description from README
• Identifies architecture patterns from code structure
• Pulls dependency information for AI context

Phase 3: Optimization

• Generates focused 33-slot IANA format
• Validates against format specification
• Scores AI-readiness with improvement suggestions

Success Metrics by Project Type

| Project Type | Avg Score | Time to Bronze | Detection Rate | |-------------|-----------|----------------|----------------| | React/Vue | 89% | Instant | 9