--- name: faf-wizard description: "FAF Wizard - One-Click AI Intelligence workflow skill. Use this skill when the user needs Done-for-you .faf generator. One-click AI context for any project - new, legacy, or famous. Auto-detects stack, scores readiness, works everywhere and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: cli-automation tags: ["faf", "automation", "project-setup", "ai-context", "productivity", "faf-wizard", "done-for-you", "generator"] complexity: advanced risk: caution tools: ["cursor", "codex-cli", "claude-code", "gemini-cli", "opencode"] source: community author: "wolfejam" date_added: "2026-04-14" date_updated: "2026-04-25" --- # FAF Wizard - One-Click AI Intelligence ## Overview This public intake copy packages `plugins/antigravity-awesome-skills-claude/skills/faf-wizard` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin. 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 the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review. # 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 | Situation | Start here | Why it matters | | --- | --- | --- | | First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow | | Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source | | Workflow execution | `SKILL.md` | Starts with the smallest copied file that materially changes execution | | Supporting context | `SKILL.md` | Adds the next most relevant copied source file without loading the entire package | | Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts | ## 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) ```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%) ``` #### Imported: 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. #### Imported: 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 | ## Examples ### Example 1: Ask for the upstream workflow directly ```text Use @faf-wizard to handle . 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 ```text 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 ```text Use @faf-wizard for . 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 ```text 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`, fails to mention provenance, or does not use any copied source files at all. **Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing. ### Problem: The imported workflow feels incomplete during review **Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task. **Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it. ### 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. | 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.** ## Related Skills - `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith-v2` - 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. | Resource family | What it gives the reviewer | Example path | | --- | --- | --- | | `references` | copied reference notes, guides, or background material from upstream | `references/n/a` | | `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` | | `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` | | `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` | | `assets` | supporting assets or schemas copied from the source package | `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? ```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 ``` #### 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 | 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% | #### 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 ```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 ``` #### 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.