wolfe-jam/faf-skills/faf-go

Claude Code and agent skill by wolfe-jam/faf-skills

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Runs `faf score`, then asks about each empty slot with AskUserQuestion, one question at a time, until the score reaches 100%. Use when someone wants to fill in what only they know about their project: "interview me", "ask me what''s missing", "get my project.faf to 100%". The answers go into project.faf; `faf sync` carries them into CLAUDE.md. Finishes the BETTER rung: every fact Claude can''t detect, supplied by the person who knows it.

Install

npx vskill@latest install wolfe-jam/faf-skills/faf-go

SKILL.md

yaml
v: 1
needs:
  - "faf-cli on Node 22+ (not installed: run `npx faf-cli <command>` in place of `faf <command>`)"
  - "AskUserQuestion (Claude Code)"
reads: [project.faf]
writes:
  - "project.faf (the answers)"
  - "CLAUDE.md (faf sync, faf's block only)"
network: ["registry.npmjs.org (read, only when npx fetches faf-cli)"]
publishes: false
done_when: "project.faf scores 100%, or every slot still empty is one the user chose to skip; CLAUDE.md re-synced by faf sync"
project: project.faf

<!-- faf:end -->

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. Every `.faf` is scored against the same 33 slots: faf-cli fills the 21 base slots and marks the 12 enterprise slots slotignored unless your app type uses them. 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 "Trophy" 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
  • Sync - Answers go into project.faf; faf sync writes them into CLAUDE.md (one way)
multiSelect Support

Some questions allow multiple selections:

  • stack.testing → "pytest + WJTTC" (informational — not one of the 33 scored slots)
  • 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 Trophy ✪ 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 (informational — not scored)
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 Trophy ✪!
 
[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: ✪ TROPHY — 100%, AI is optimized to code!
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 Trophy ✪ 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% Trophy — AI is optimized to code
 
project.faf: complete
CLAUDE.md:   run `faf sync` to update

Optionally run faf sync to emit CLAUDE.md (and faf export --agents for 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, sync, the always-33 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.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

See the full security report

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

Popularity

Repository stars
4
7-day trend score
8

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/wolfe-jam/faf-skills/faf-go/badge)](https://verified-skill.com/skills/wolfe-jam/faf-skills/faf-go)

Something wrong with this skill or its page? Tell us.