---
name: faf-wizard-24582f1233b3
description: "Done-for-you .faf generator. One-click AI context for any project - new, legacy, or famous. Auto-detects stack, scores readiness, works everywhere."
category: productivity
risk: safe
source: community
source_repo: Wolfe-Jam/faf-skills
source_type: community
date_added: "2026-04-07"
author: wolfejam
tags: [faf, automation, project-setup, ai-context, productivity]
tools: [claude, cursor, gemini, windsurf, any-ai]
---

# 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

FAF scores completeness of its own context schema; it does not measure whether an AI can understand a repository or whether a project is production ready.

| 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 |

## Illustrative before/after scenarios

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

### Step 2: Generation (30 seconds)  
```yaml
# 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%)
```

## Upstream performance claims (not independently verified here)

**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** - Read or export the context through its supported project instructions; do not assume native `.faf` loading
- ✅ **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?
```bash
# 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)
```bash
npm install -g faf-cli
cd your-project
faf auto
```

### Option 2: MCP Server (Claude Code)
```json
{
  "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 | 99.8% |
| **Python Django** | 91% | Instant | 99.5% |  
| **Rust CLI** | 85% | Instant | 99.1% |
| **Legacy PHP** | 76% | 30 seconds | 94.2% |
| **Monorepo** | 82% | 45 seconds | 91.8% |

## When to Use faf-expert Instead

Use `faf-wizard` for:
- ✅ Quick project onboarding
- ✅ Automatic everything
- ✅ "Just make it work"
- ✅ Time-constrained scenarios

Use `faf-expert` for:
- 🎯 Fine-tuned championship scoring (95%+)
- 🎯 Complex MCP server configuration
- 🎯 Multi-platform sync management  
- 🎯 Enterprise deployment patterns

## Validation & Security

**Upstream quality and packaging claims — verify against the selected version:**
- ✅ **800+ comprehensive tests** across CLI and MCP
- ✅ **No credentials ever stored** in .faf files
- ✅ **YAML format validation** prevents malformed files
- ✅ **IANA-registered format** (application/vnd.faf+yaml)
- ✅ **MIT licensed** - safe for commercial use

## Getting Started

### For Your Current Project
```bash
# One command, done forever
npx faf-cli auto

# Check the results
cat project.faf
```

### For Any GitHub Repository  
Install the browser extension and click "Generate FAF" on any repo.

### For Teams
```bash
# Set up team-wide MCP server
faf mcp install --team
faf sync --target all --watch
```

## Community & Support

- **Website**: https://faf.one
- **Chrome Extension**: 4.8★ rating, Google approved
- **Downloads**: 52k+ across ecosystem  
- **Discord**: Active community of 1000+ developers
- **Documentation**: Comprehensive guides and examples

---

*Stop explaining your project every session. FAF Wizard - because AI should understand your project as well as you do.*

## 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.

Before generating or syncing, inspect existing `project.faf`, `CLAUDE.md`, `AGENTS.md` and other target instruction files. Preserve their user-authored rules, preview the diff and apply only scoped changes; `sync --target all` is not authorization to overwrite unrelated instructions. Never place credentials or confidential source in context files. Timing, adoption and accuracy figures above are upstream claims, not evidence established by this catalog. [Upstream integration model](https://github.com/Wolfe-Jam/faf-skills#works-with-claudemd).


---
Source attribution: wolfejam
Original contribution (preserved): https://github.com/sickn33/antigravity-awesome-skills/blob/6f819e34e3847c4a9a4715409ec4d76b0ad42bdb/skills/faf-wizard/SKILL.md
License: MIT + CC-BY-4.0
License information: https://creativecommons.org/licenses/by/4.0/
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Antigravity User

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.


Creative Commons Attribution 4.0 International (CC BY 4.0)

This file clarifies the license for original non-code repository content.

- Original code and tooling remain licensed under the MIT License in `LICENSE`.
- Original documentation and other non-code written content are licensed under
  the Creative Commons Attribution 4.0 International license unless a more
  specific upstream notice says otherwise.
- Third-party attributions, upstream licenses, and exceptions are tracked in
  `docs/sources/sources.md`.

Canonical license URL:
https://creativecommons.org/licenses/by/4.0/

Review the full license text at the canonical URL before reuse.



# Mega AI Hub resource metadata

Creator / publisher: wolfejam
Original contribution (preserved): https://github.com/sickn33/antigravity-awesome-skills/blob/6f819e34e3847c4a9a4715409ec4d76b0ad42bdb/skills/faf-wizard/SKILL.md
Attribution basis: Explicit original-content license; package is not listed among upstream exceptions

Fields of work: Product Management, Development & Engineering
Roles: Product Manager, Growth Product Manager, Frontend Developer, Backend Developer, DevOps / SRE
Specialties: API, Backend, Frontend
Tools: Any Ai
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: 67455e5f537c714017c5b351fab0b48160f7fb5b5202a2beecd2e243594f5b41