Guided interview to Gold Code (100% AI-Readiness). Use when helping users improve their .faf file through questions. Leverages Claude Code's AskUserQuestion for seamless integration. Just type /faf-go and answer questions till done.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Guided interview to Gold Code (100% AI-Readiness). Use when helping users improve their .faf file through questions. Leverages Claude Code's AskUserQuestion for seamless integration. Just type /faf-go and answer questions till done.
"Just type /faf-go, answer questions till you're done. 100% target."
.faf is an IANA-registered context format (application/vnd.faf+yaml) — a typed, portable file you own, readable by any AI. faf-cli scores on 21 slots; your app_type selects which are active, and 100% ✪ = every active slot filled. This skill is the guided interview that gets you there: the AI fills what it can detect, then asks you — via Claude Code's AskUserQuestion — only for the gaps it can't source.
When to Use This Skill
Activate when:
User wants to improve their .faf score
User mentions "Gold Code" or "100%"
User has incomplete project context
After faf init to fill in missing fields
User says "help me with my .faf"
Integration with Claude Code
FAF Go is built FOR Claude Code:
AskUserQuestion - Native Claude Code UI for questions
Structured output - JSON that Claude Code understands
Bi-sync - Answers flow to .faf AND CLAUDE.md
multiSelect Support
Some questions allow multiple selections:
stack.testing → "pytest + WJTTC"
stack.cicd → "GitHub Actions + Cloud Build"
stack.frontend → "React + Tailwind"
human_context.who → "Developers + AI agents"
When multiSelect: true, user can pick 2+ options. Results are joined with " + ".
Workflow
Step 1: Check Current State
Run faf score to understand current position:
faf score --verbose
Or get it as structured data for programmatic use:
faf score --json
--json returns the score + per-slot breakdown — the empty slots are what you interview on (the priority order is in Step 2).
Step 2: Ask Questions Using AskUserQuestion
For each missing field, use Claude Code's AskUserQuestion tool:
Priority Order (most impactful first):
project.goal - What does this project do?
human_context.why - Why does this exist?
human_context.who - Who uses this?
human_context.what - What problem does it solve?
project.main_language - Primary language
stack.database - Database choice
stack.hosting - Where is it deployed?
stack.frontend - Frontend framework
stack.backend - Backend framework
human_context.where - Environment
human_context.when - Timeline/phase
human_context.how - How the project is built (sourced from the stack)
Step 3: Apply Answers
After collecting answers, update the .faf file:
# Read current .fafcat project.faf
# Update fields (use Edit tool)# Then verify:
faf score
Step 4: Celebrate or Continue
If score >= 100: Celebrate Gold Code achievement
If score < 100: Continue with remaining questions
Question Templates for AskUserQuestion
Single-Select Questions (pick one)
project.goal
{"question":"What does this project do? (one clear sentence)","header":"Goal","multiSelect":false,"options":[{"label":"Let me type it","description":"I'll describe it myself"},{"label":"Help me write it","description":"Guide me through it"}]}
human_context.why
{"question":"Why does this project exist?","header":"Why","multiSelect":false,"options":[{"label":"Business need","description":"Solving a business problem"},{"label":"Personal project","description":"Learning or hobby"},{"label":"Open source","description":"Community contribution"},{"label":"Let me explain","description":"Custom reason"}]}
stack.database
{"question":"What database do you use?","header":"Database","multiSelect":false,"options":[{"label":"PostgreSQL","description":"Relational database"},{"label":"MongoDB","description":"Document database"},{"label":"SQLite","description":"File-based database"},{"label":"None","description":"No database"}]}
stack.hosting
{"question":"Where is this deployed?","header":"Hosting","multiSelect":false,"options":[{"label":"Vercel","description":"Frontend/serverless"},{"label":"AWS","description":"Amazon Web Services"},{"label":"Local only","description":"Not deployed"},{"label":"Other","description":"Different platform"}]}
Multi-Select Questions (pick multiple, joined with " + ")
stack.testing
{"question":"What testing tools/methodologies do you use?","header":"Testing","multiSelect":true,"options":[{"label":"pytest","description":"Python testing framework"},{"label":"Jest","description":"JavaScript testing"},{"label":"Vitest","description":"Vite-native testing"},{"label":"WJTTC","description":"Championship methodology (Layer 2)"}]}
Result format:pytest + WJTTC (industry first, WJTTC follows)
Ordering: When both selected, industry tests come first:
pytest + WJTTC (not WJTTC + pytest)
WJTTC can also run standalone
stack.cicd
{"question":"What CI/CD tools do you use?","header":"CI/CD","multiSelect":true,"options":[{"label":"GitHub Actions","description":"GitHub-native CI/CD"},{"label":"Cloud Build","description":"Google Cloud CI/CD"},{"label":"CircleCI","description":"CircleCI pipelines"},{"label":"None","description":"No CI/CD yet"}]}
Result format:GitHub Actions + Cloud Build
stack.frontend
{"question":"What frontend technologies do you use?","header":"Frontend","multiSelect":true,"options":[{"label":"React","description":"React framework"},{"label":"Next.js","description":"React meta-framework"},{"label":"Svelte","description":"Svelte framework"},{"label":"None/API-only","description":"No frontend"}]}
human_context.who
{"question":"Who uses this project?","header":"Users","multiSelect":true,"options":[{"label":"Developers","description":"Software developers"},{"label":"End users","description":"Non-technical users"},{"label":"AI agents","description":"Claude, Gemini, etc."},{"label":"Internal team","description":"Your team only"}]}
Result format:Developers + AI agents
Processing Multi-Select Answers
When user selects multiple options, join them with " + ":
# Example: User selects ["pytest", "WJTTC"]
selected = ["pytest", "WJTTC"]
value = " + ".join(selected) # "pytest + WJTTC"
This creates readable, scannable values in the .faf file:
User: /faf-go
Claude: Let me check your current .faf status.
[Runs: faf score --verbose]
Your score is 45%. Let's get you to Gold Code!
[Uses AskUserQuestion for project.goal]
User: [Selects option or types custom]
Claude: Great! Now let's capture why this project exists.
[Uses AskUserQuestion for human_context.why]
... continues until 100% ...
Claude: ✪ GOLD CODE ACHIEVED!
Your AI now has complete context for championship performance.
Outside Claude Code, the same destination is reached with the CLI's own interactive interview:
faf go # interactive terminal interview (--resume continues a session)
This skill is the Claude-native version of that interview — AskUserQuestion instead of terminal prompts. For structured, programmatic data, use faf score --json.
Success Metrics
User reaches 100% score
All required fields filled with meaningful content
No placeholder values (TBD, Unknown, None where inappropriate)
