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.
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.
This public intake copy packages plugins/antigravity-awesome-skills/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.
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
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
Use @faf-wizard-v2 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-v2 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-v2 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-v2 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/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
