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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.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/ChonSong/skill-retriever --skill faf-goコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
AI-powered Draw.io diagram generation with real-time browser preview. Create flowcharts, architecture diagrams, sequence diagrams, and cloud infrastructure diagrams (AWS/GCP/Azure) using natural language. Supports animated connectors, real-time editing, and structured A–H format extraction from text or images.
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this,"...
SOC 職業分類に基づく
SKILL.md を表示中
| name | faf-go |
| 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. |
| risk | unknown |
| source | https://github.com/Wolfe-Jam/faf-skills/tree/main/skills/faf-go |
| source_repo | Wolfe-Jam/faf-skills |
| source_type | community |
| date_added | 2026-07-01T00:00:00.000Z |
| license | MIT |
| license_source | https://github.com/Wolfe-Jam/faf-skills/blob/main/LICENSE |
"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.
Activate when:
faf init to fill in missing fieldsFAF Go is built FOR Claude Code:
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 " + ".
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).
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 languagestack.database - Database choicestack.hosting - Where is it deployed?stack.frontend - Frontend frameworkstack.backend - Backend frameworkhuman_context.where - Environmenthuman_context.when - Timeline/phasehuman_context.how - How the project is built (sourced from the stack)After collecting answers, update the .faf file:
# Read current .faf
cat project.faf
# Update fields (use Edit tool)
# Then verify:
faf score
If score >= 100: Celebrate Gold Code achievement If score < 100: Continue with remaining questions
{
"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"}
]
}
{
"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"}
]
}
{
"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"}
]
}
{
"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"}
]
}
{
"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){
"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
{
"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"}
]
}
{
"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
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
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.
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"}
]
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.
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.
.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.