sickn33 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 |