📦 deps(thirdparty): update snapshots
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---
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name: faf-go
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description: 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.
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risk: unknown
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source: https://github.com/Wolfe-Jam/faf-skills/tree/main/skills/faf-go
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source_repo: Wolfe-Jam/faf-skills
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source_type: community
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date_added: 2026-07-01
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license: MIT
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license_source: https://github.com/Wolfe-Jam/faf-skills/blob/main/LICENSE
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---
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# FAF Go — Guided Path to 100% ✪
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**"Just type /faf-go, answer questions till you're done. 100% target."**
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`.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.
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## When to Use This Skill
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Activate when:
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- User wants to improve their .faf score
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- User mentions "Gold Code" or "100%"
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- User has incomplete project context
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- After `faf init` to fill in missing fields
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- User says "help me with my .faf"
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## Integration with Claude Code
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FAF Go is built FOR Claude Code:
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- **AskUserQuestion** - Native Claude Code UI for questions
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- **multiSelect: true** - Allow multiple answers (e.g., "pytest + WJTTC")
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- **TodoWrite** - Track progress through the interview
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- **Structured output** - JSON that Claude Code understands
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- **Bi-sync** - Answers flow to .faf AND CLAUDE.md
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### multiSelect Support
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Some questions allow multiple selections:
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- `stack.testing` → "pytest + WJTTC"
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- `stack.cicd` → "GitHub Actions + Cloud Build"
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- `stack.frontend` → "React + Tailwind"
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- `human_context.who` → "Developers + AI agents"
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When `multiSelect: true`, user can pick 2+ options. Results are joined with " + ".
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## Workflow
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### Step 1: Check Current State
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Run faf score to understand current position:
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```bash
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faf score --verbose
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```
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Or get it as structured data for programmatic use:
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```bash
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faf score --json
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```
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`--json` returns the score + per-slot breakdown — the empty slots are what you interview on (the priority order is in Step 2).
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### Step 2: Ask Questions Using AskUserQuestion
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For each missing field, use Claude Code's AskUserQuestion tool:
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**Priority Order (most impactful first):**
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1. `project.goal` - What does this project do?
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2. `human_context.why` - Why does this exist?
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3. `human_context.who` - Who uses this?
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4. `human_context.what` - What problem does it solve?
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5. `project.main_language` - Primary language
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6. `stack.database` - Database choice
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7. `stack.hosting` - Where is it deployed?
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8. `stack.frontend` - Frontend framework
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9. `stack.backend` - Backend framework
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10. `human_context.where` - Environment
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11. `human_context.when` - Timeline/phase
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12. `human_context.how` - How the project is built (sourced from the stack)
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### Step 3: Apply Answers
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After collecting answers, update the .faf file:
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```bash
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# Read current .faf
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cat project.faf
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# Update fields (use Edit tool)
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# Then verify:
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faf score
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```
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### Step 4: Celebrate or Continue
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If score >= 100: Celebrate Gold Code achievement
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If score < 100: Continue with remaining questions
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## Question Templates for AskUserQuestion
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### Single-Select Questions (pick one)
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#### project.goal
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```json
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{
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"question": "What does this project do? (one clear sentence)",
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"header": "Goal",
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"multiSelect": false,
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"options": [
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{"label": "Let me type it", "description": "I'll describe it myself"},
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{"label": "Help me write it", "description": "Guide me through it"}
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]
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}
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```
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#### human_context.why
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```json
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{
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"question": "Why does this project exist?",
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"header": "Why",
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"multiSelect": false,
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"options": [
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{"label": "Business need", "description": "Solving a business problem"},
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{"label": "Personal project", "description": "Learning or hobby"},
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{"label": "Open source", "description": "Community contribution"},
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{"label": "Let me explain", "description": "Custom reason"}
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]
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}
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```
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#### stack.database
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```json
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{
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"question": "What database do you use?",
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"header": "Database",
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"multiSelect": false,
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"options": [
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{"label": "PostgreSQL", "description": "Relational database"},
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{"label": "MongoDB", "description": "Document database"},
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{"label": "SQLite", "description": "File-based database"},
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{"label": "None", "description": "No database"}
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]
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}
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```
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#### stack.hosting
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```json
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{
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"question": "Where is this deployed?",
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"header": "Hosting",
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"multiSelect": false,
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"options": [
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{"label": "Vercel", "description": "Frontend/serverless"},
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{"label": "AWS", "description": "Amazon Web Services"},
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{"label": "Local only", "description": "Not deployed"},
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{"label": "Other", "description": "Different platform"}
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]
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}
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```
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### Multi-Select Questions (pick multiple, joined with " + ")
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#### stack.testing
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```json
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{
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"question": "What testing tools/methodologies do you use?",
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"header": "Testing",
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"multiSelect": true,
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"options": [
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{"label": "pytest", "description": "Python testing framework"},
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{"label": "Jest", "description": "JavaScript testing"},
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{"label": "Vitest", "description": "Vite-native testing"},
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{"label": "WJTTC", "description": "Championship methodology (Layer 2)"}
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]
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}
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```
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**Result format:** `pytest + WJTTC` (industry first, WJTTC follows)
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**Ordering:** When both selected, industry tests come first:
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- `pytest + WJTTC` (not `WJTTC + pytest`)
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- WJTTC can also run standalone
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#### stack.cicd
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```json
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{
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"question": "What CI/CD tools do you use?",
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"header": "CI/CD",
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"multiSelect": true,
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"options": [
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{"label": "GitHub Actions", "description": "GitHub-native CI/CD"},
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{"label": "Cloud Build", "description": "Google Cloud CI/CD"},
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{"label": "CircleCI", "description": "CircleCI pipelines"},
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{"label": "None", "description": "No CI/CD yet"}
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]
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}
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```
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**Result format:** `GitHub Actions + Cloud Build`
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#### stack.frontend
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```json
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{
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"question": "What frontend technologies do you use?",
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"header": "Frontend",
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"multiSelect": true,
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"options": [
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{"label": "React", "description": "React framework"},
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{"label": "Next.js", "description": "React meta-framework"},
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{"label": "Svelte", "description": "Svelte framework"},
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{"label": "None/API-only", "description": "No frontend"}
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]
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}
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```
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#### human_context.who
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```json
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{
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"question": "Who uses this project?",
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"header": "Users",
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"multiSelect": true,
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"options": [
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{"label": "Developers", "description": "Software developers"},
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{"label": "End users", "description": "Non-technical users"},
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{"label": "AI agents", "description": "Claude, Gemini, etc."},
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{"label": "Internal team", "description": "Your team only"}
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]
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}
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```
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**Result format:** `Developers + AI agents`
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### Processing Multi-Select Answers
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When user selects multiple options, join them with " + ":
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```python
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# Example: User selects ["pytest", "WJTTC"]
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selected = ["pytest", "WJTTC"]
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value = " + ".join(selected) # "pytest + WJTTC"
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```
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This creates readable, scannable values in the .faf file:
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```yaml
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stack:
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testing: pytest + WJTTC
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cicd: GitHub Actions + Cloud Build
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```
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## Example Session
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```
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User: /faf-go
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Claude: Let me check your current .faf status.
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[Runs: faf score --verbose]
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Your score is 45%. Let's get you to Gold Code!
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[Uses AskUserQuestion for project.goal]
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User: [Selects option or types custom]
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Claude: Great! Now let's capture why this project exists.
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[Uses AskUserQuestion for human_context.why]
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... continues until 100% ...
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Claude: ✪ GOLD CODE ACHIEVED!
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Your AI now has complete context for championship performance.
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```
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## TodoWrite Integration
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Track progress with todos:
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```javascript
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[
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{"content": "Answer project.goal question", "status": "completed"},
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{"content": "Answer human_context.why question", "status": "in_progress"},
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{"content": "Answer stack.database question", "status": "pending"},
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{"content": "Verify Gold Code achieved", "status": "pending"}
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]
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```
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## CLI Fallback
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Outside Claude Code, the same destination is reached with the CLI's own interactive interview:
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```bash
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faf go # interactive terminal interview (--resume continues a session)
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```
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This skill is the **Claude-native** version of that interview — AskUserQuestion instead of terminal prompts. For structured, programmatic data, use `faf score --json`.
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## Success Metrics
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- User reaches 100% score
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- All required fields filled with meaningful content
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- No placeholder values (TBD, Unknown, None where inappropriate)
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- User understands what each field is for
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## On Completion
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When 100% ✪ is achieved:
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```
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✪ 100% — Gold Code
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project.faf: complete
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CLAUDE.md: synced from .faf
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```
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Optionally run `faf sync` to emit CLAUDE.md / AGENTS.md from the .faf. Your AI now starts every session with complete project context.
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## Related Skills
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- **faf-context** — the builder's quickstart: hand the AI what it needs to hit 100%, fast
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- **faf-wizard** — done-for-you, one-click .faf for any project
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- **faf-expert** — master the format: scoring internals, MCP config, bi-sync, the full 21-slot model
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---
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> .faf is the format. project.faf is the file.
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> 100% ✪ AI-Readiness is the result.
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---
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*MIT · part of the FAF skill family (faf-context · faf-wizard · faf-expert). Native to Claude Code.*
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## Limitations
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- Use this skill only when the task clearly matches its upstream source and local project context.
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- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
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- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
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