FAF Wizard - One-Click AI Intelligence

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/faf-wizard

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

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

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.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Works on ANY Project, Real Success Stories, Performance Data (Real Numbers), Universal Compatibility, Three-Phase Intelligence, Success Metrics by Project Type.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• ✅ Quick project onboarding
• ✅ Automatic everything
• ✅ "Just make it work"
• ✅ Time-constrained scenarios
• 🎯 Fine-tuned championship scoring (95%+)
• 🎯 Complex MCP server configuration

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. 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%) ### 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.

2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: 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%)

Imported: 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.

Imported: Works on ANY Project

Examples

Example 1: Ask for the upstream workflow directly

Use @faf-wizard to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @faf-wizard against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @faf-wizard for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @faf-wizard using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/faf-wizard

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: The Problem It Solves

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

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

Related Skills

• @devops-deploy

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @devops-troubleshooter

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @differential-review

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @discord-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: 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

Imported: 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

Imported: 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

Imported: 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

Imported: Success Metrics by Project Type

Imported: Validation & Security

Enterprise-Grade Standards:

• ✅ 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

Imported: Getting Started

For Your Current Project

# 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

# Set up team-wide MCP server
faf mcp install --team
faf sync --target all --watch

Imported: 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.

Imported: 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.