FAF Go — Guided Path to 100% ✪

"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
• multiSelect: true - Allow multiple answers (e.g., "pytest + WJTTC")
• TodoWrite - Track progress through the interview
• 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:

bash
faf score --verbose

Or get it as structured data for programmatic use:

bash
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):

1. project.goal - What does this project do?
2. human_context.why - Why does this exist?
3. human_context.who - Who uses this?
4. human_context.what - What problem does it solve?
5. project.main_language - Primary language
6. stack.database - Database choice
7. stack.hosting - Where is it deployed?
8. stack.frontend - Frontend framework
9. stack.backend - Backend framework
10. human_context.where - Environment
11. human_context.when - Timeline/phase
12. human_context.how - How the project is built (sourced from the stack)

Step 3: Apply Answers

After collecting answers, update the .faf file:

bash
# Read current .faf
cat 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

json
{
  "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

json
{
  "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

json
{
  "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

json
{
  "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

json
{
  "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

json
{
  "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

json
{
  "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

json
{
  "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 " + ":

python
# Example: User selects ["pytest", "WJTTC"]
selected = ["pytest", "WJTTC"]
value = " + ".join(selected)  # "pytest + WJTTC"

This creates readable, scannable values in the .faf file:

yaml
stack:
  testing: pytest + WJTTC
  cicd: GitHub Actions + Cloud Build

Example Session

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.

TodoWrite Integration

Track progress with todos:

javascript
[
  {"content": "Answer project.goal question", "status": "completed"},
  {"content": "Answer human_context.why question", "status": "in_progress"},
  {"content": "Answer stack.database question", "status": "pending"},
  {"content": "Verify Gold Code achieved", "status": "pending"}
]

CLI Fallback

Outside Claude Code, the same destination is reached with the CLI's own interactive interview:

bash
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)
• User understands what each field is for

On Completion

When 100% ✪ is achieved:

✪ 100% — Gold Code

project.faf: complete
CLAUDE.md:   synced from .faf

Optionally run faf sync to emit CLAUDE.md / AGENTS.md from the .faf. Your AI now starts every session with complete project context.

Related Skills

• faf-context — the builder's quickstart: hand the AI what it needs to hit 100%, fast
• faf-wizard — done-for-you, one-click .faf for any project
• faf-expert — master the format: scoring internals, MCP config, bi-sync, the full 21-slot model

> .faf is the format. project.faf is the file. > 100% ✪ AI-Readiness is the result.

MIT · part of the FAF skill family (faf-context · faf-wizard · faf-expert). Native to Claude Code.

Limitations

• Use this skill only when the task clearly matches its upstream source and local project context.
• Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
• Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.