faf-go – Coding skill for AI agents

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 " + ".

Score and instruction-file boundaries

A 100% score measures filled slots under the installed CLI rules, not complete or correct project knowledge. Keep unknowns explicit instead of inventing answers to improve the score. Use the question tools actually available in the host; the examples below are Claude-specific shapes. Before any faf sync, inspect its diff/output paths and preserve existing CLAUDE.md/AGENTS.md instructions and user edits; filling context does not authorize overwriting repository policy or syncing secrets into agent-readable files. Report sync only if it actually ran and its output was checked.

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):

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:

# 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

{
  "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:

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!
All active scoring slots are filled; accuracy and project coverage still need review.

TodoWrite Integration

Track progress with todos:

[
  {"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:

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
`

… (full content is in the download package)