[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F6915RGXYFSZB1WPAH358R":3,"skill-same:01M6F6915RGXYFSZB1WPAH358R":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":35,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6F6915RGXYFSZB1WPAH358R","faf-go",null,"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 \u002Ffaf-go and answer questions till done.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-faf-go\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"go","faf go","faf","2026","when","jam","gold","for","tree","answer","questions","users","readiness","critical","type","done","wed","main","community","use","interview","just","improve","file","license","code","github","2026-11-10 10:12:30",false,"---\nname: faf-go\ndescription: 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 \u002Ffaf-go and answer questions till done.\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002FWolfe-Jam\u002Ffaf-skills\u002Ftree\u002Fmain\u002Fskills\u002Ffaf-go\nsource_repo: Wolfe-Jam\u002Ffaf-skills\nsource_type: community\ndate_added: 2026-07-01\nlicense: MIT\nlicense_source: https:\u002F\u002Fgithub.com\u002FWolfe-Jam\u002Ffaf-skills\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# FAF Go — Guided Path to 100% ✪\n\n**\"Just type \u002Ffaf-go, answer questions till you're done. 100% target.\"**\n\n`.faf` is an **IANA-registered context format** (`application\u002Fvnd.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.\n\n## When to Use This Skill\n\nActivate when:\n- User wants to improve their .faf score\n- User mentions \"Gold Code\" or \"100%\"\n- User has incomplete project context\n- After `faf init` to fill in missing fields\n- User says \"help me with my .faf\"\n\n## Integration with Claude Code\n\nFAF Go is built FOR Claude Code:\n- **AskUserQuestion** - Native Claude Code UI for questions\n- **multiSelect: true** - Allow multiple answers (e.g., \"pytest + WJTTC\")\n- **TodoWrite** - Track progress through the interview\n- **Structured output** - JSON that Claude Code understands\n- **Bi-sync** - Answers flow to .faf AND CLAUDE.md\n\n### multiSelect Support\n\nSome questions allow multiple selections:\n- `stack.testing` → \"pytest + WJTTC\"\n- `stack.cicd` → \"GitHub Actions + Cloud Build\"\n- `stack.frontend` → \"React + Tailwind\"\n- `human_context.who` → \"Developers + AI agents\"\n\nWhen `multiSelect: true`, user can pick 2+ options. Results are joined with \" + \".\n\n## Score and instruction-file boundaries\n\nA 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\u002Foutput paths and preserve existing CLAUDE.md\u002FAGENTS.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.\n\n## Workflow\n\n### Step 1: Check Current State\n\nRun faf score to understand current position:\n\n```bash\nfaf score --verbose\n```\n\nOr get it as structured data for programmatic use:\n\n```bash\nfaf score --json\n```\n\n`--json` returns the score + per-slot breakdown — the empty slots are what you interview on (the priority order is in Step 2).\n\n### Step 2: Ask Questions Using AskUserQuestion\n\nFor each missing field, use Claude Code's AskUserQuestion tool:\n\n**Priority Order (most impactful first):**\n1. `project.goal` - What does this project do?\n2. `human_context.why` - Why does this exist?\n3. `human_context.who` - Who uses this?\n4. `human_context.what` - What problem does it solve?\n5. `project.main_language` - Primary language\n6. `stack.database` - Database choice\n7. `stack.hosting` - Where is it deployed?\n8. `stack.frontend` - Frontend framework\n9. `stack.backend` - Backend framework\n10. `human_context.where` - Environment\n11. `human_context.when` - Timeline\u002Fphase\n12. `human_context.how` - How the project is built (sourced from the stack)\n\n### Step 3: Apply Answers\n\nAfter collecting answers, update the .faf file:\n\n```bash\n# Read current .faf\ncat project.faf\n\n# Update fields (use Edit tool)\n# Then verify:\nfaf score\n```\n\n### Step 4: Celebrate or Continue\n\nIf score >= 100: Celebrate Gold Code achievement\nIf score \u003C 100: Continue with remaining questions\n\n## Question Templates for AskUserQuestion\n\n### Single-Select Questions (pick one)\n\n#### project.goal\n```json\n{\n  \"question\": \"What does this project do? (one clear sentence)\",\n  \"header\": \"Goal\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Let me type it\", \"description\": \"I'll describe it myself\"},\n    {\"label\": \"Help me write it\", \"description\": \"Guide me through it\"}\n  ]\n}\n```\n\n#### human_context.why\n```json\n{\n  \"question\": \"Why does this project exist?\",\n  \"header\": \"Why\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Business need\", \"description\": \"Solving a business problem\"},\n    {\"label\": \"Personal project\", \"description\": \"Learning or hobby\"},\n    {\"label\": \"Open source\", \"description\": \"Community contribution\"},\n    {\"label\": \"Let me explain\", \"description\": \"Custom reason\"}\n  ]\n}\n```\n\n#### stack.database\n```json\n{\n  \"question\": \"What database do you use?\",\n  \"header\": \"Database\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"PostgreSQL\", \"description\": \"Relational database\"},\n    {\"label\": \"MongoDB\", \"description\": \"Document database\"},\n    {\"label\": \"SQLite\", \"description\": \"File-based database\"},\n    {\"label\": \"None\", \"description\": \"No database\"}\n  ]\n}\n```\n\n#### stack.hosting\n```json\n{\n  \"question\": \"Where is this deployed?\",\n  \"header\": \"Hosting\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Vercel\", \"description\": \"Frontend\u002Fserverless\"},\n    {\"label\": \"AWS\", \"description\": \"Amazon Web Services\"},\n    {\"label\": \"Local only\", \"description\": \"Not deployed\"},\n    {\"label\": \"Other\", \"description\": \"Different platform\"}\n  ]\n}\n```\n\n### Multi-Select Questions (pick multiple, joined with \" + \")\n\n#### stack.testing\n```json\n{\n  \"question\": \"What testing tools\u002Fmethodologies do you use?\",\n  \"header\": \"Testing\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"pytest\", \"description\": \"Python testing framework\"},\n    {\"label\": \"Jest\", \"description\": \"JavaScript testing\"},\n    {\"label\": \"Vitest\", \"description\": \"Vite-native testing\"},\n    {\"label\": \"WJTTC\", \"description\": \"Championship methodology (Layer 2)\"}\n  ]\n}\n```\n**Result format:** `pytest + WJTTC` (industry first, WJTTC follows)\n\n**Ordering:** When both selected, industry tests come first:\n- `pytest + WJTTC` (not `WJTTC + pytest`)\n- WJTTC can also run standalone\n\n#### stack.cicd\n```json\n{\n  \"question\": \"What CI\u002FCD tools do you use?\",\n  \"header\": \"CI\u002FCD\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"GitHub Actions\", \"description\": \"GitHub-native CI\u002FCD\"},\n    {\"label\": \"Cloud Build\", \"description\": \"Google Cloud CI\u002FCD\"},\n    {\"label\": \"CircleCI\", \"description\": \"CircleCI pipelines\"},\n    {\"label\": \"None\", \"description\": \"No CI\u002FCD yet\"}\n  ]\n}\n```\n**Result format:** `GitHub Actions + Cloud Build`\n\n#### stack.frontend\n```json\n{\n  \"question\": \"What frontend technologies do you use?\",\n  \"header\": \"Frontend\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"React\", \"description\": \"React framework\"},\n    {\"label\": \"Next.js\", \"description\": \"React meta-framework\"},\n    {\"label\": \"Svelte\", \"description\": \"Svelte framework\"},\n    {\"label\": \"None\u002FAPI-only\", \"description\": \"No frontend\"}\n  ]\n}\n```\n\n#### human_context.who\n```json\n{\n  \"question\": \"Who uses this project?\",\n  \"header\": \"Users\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"Developers\", \"description\": \"Software developers\"},\n    {\"label\": \"End users\", \"description\": \"Non-technical users\"},\n    {\"label\": \"AI agents\", \"description\": \"Claude, Gemini, etc.\"},\n    {\"label\": \"Internal team\", \"description\": \"Your team only\"}\n  ]\n}\n```\n**Result format:** `Developers + AI agents`\n\n### Processing Multi-Select Answers\n\nWhen user selects multiple options, join them with \" + \":\n\n```python\n# Example: User selects [\"pytest\", \"WJTTC\"]\nselected = [\"pytest\", \"WJTTC\"]\nvalue = \" + \".join(selected)  # \"pytest + WJTTC\"\n```\n\nThis creates readable, scannable values in the .faf file:\n```yaml\nstack:\n  testing: pytest + WJTTC\n  cicd: GitHub Actions + Cloud Build\n```\n\n## Example Session\n\n```\nUser: \u002Ffaf-go\n\nClaude: Let me check your current .faf status.\n\n[Runs: faf score --verbose]\n\nYour score is 45%. Let's get you to Gold Code!\n\n[Uses AskUserQuestion for project.goal]\n\nUser: [Selects option or types custom]\n\nClaude: Great! Now let's capture why this project exists.\n\n[Uses AskUserQuestion for human_context.why]\n\n... continues until 100% ...\n\nClaude: ✪ GOLD CODE ACHIEVED!\nAll active scoring slots are filled; accuracy and project coverage still need review.\n```\n\n## TodoWrite Integration\n\nTrack progress with todos:\n\n```javascript\n[\n  {\"content\": \"Answer project.goal question\", \"status\": \"completed\"},\n  {\"content\": \"Answer human_context.why question\", \"status\": \"in_progress\"},\n  {\"content\": \"Answer stack.database question\", \"status\": \"pending\"},\n  {\"content\": \"Verify Gold Code achieved\", \"status\": \"pending\"}\n]\n```\n\n## CLI Fallback\n\nOutside Claude Code, the same destination is reached with the CLI's own interactive interview:\n\n```bash\nfaf go            # interactive terminal interview (--resume continues a session)\n```\n\nThis skill is the **Claude-native** version of that interview — AskUserQuestion instead of terminal prompts. For structured, programmatic data, use `faf score --json`.\n\n## Success Metrics\n\n- User reaches 100% score\n- All required fields filled with meaningful content\n- No placeholder values (TBD, Unknown, None where inappropriate)\n- User understands what each field is for\n\n## On Completion\n\nWhen 100% ✪ is achieved:\n\n```\n✪ 100% — Gold Code\n\nproject.faf: complete\nCLAUDE.md:   synced from .faf\n```\n\nOptionally run `faf sync` to emit CLAUDE.md \u002F AGENTS.md from the .faf. Your AI now starts every session with complete project context.\n\n## Related Skills\n\n- **faf-context** — the builder's quickstart: hand the AI what it needs to hit 100%, fast\n- **faf-wizard** — done-for-you, one-click .faf for any project\n- **faf-expert** — master the format: scoring internals, MCP config, bi-sync, the full 21-slot model\n\n---\n\n> .faf is the format. project.faf is the file.\n> 100% ✪ AI-Readiness is the result.\n\n---\n\n*MIT · part of the FAF skill family (faf-context · faf-wizard · faf-expert). Native to Claude Code.*\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream source and local project context.\n- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.\n- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n","# FAF Go — Guided Path to 100% ✪\n\n**\"Just type \u002Ffaf-go, answer questions till you're done. 100% target.\"**\n\n`.faf` is an **IANA-registered context format** (`application\u002Fvnd.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.\n\n## When to Use This Skill\n\nActivate when:\n- User wants to improve their .faf score\n- User mentions \"Gold Code\" or \"100%\"\n- User has incomplete project context\n- After `faf init` to fill in missing fields\n- User says \"help me with my .faf\"\n\n## Integration with Claude Code\n\nFAF Go is built FOR Claude Code:\n- **AskUserQuestion** - Native Claude Code UI for questions\n- **multiSelect: true** - Allow multiple answers (e.g., \"pytest + WJTTC\")\n- **TodoWrite** - Track progress through the interview\n- **Structured output** - JSON that Claude Code understands\n- **Bi-sync** - Answers flow to .faf AND CLAUDE.md\n\n### multiSelect Support\n\nSome questions allow multiple selections:\n- `stack.testing` → \"pytest + WJTTC\"\n- `stack.cicd` → \"GitHub Actions + Cloud Build\"\n- `stack.frontend` → \"React + Tailwind\"\n- `human_context.who` → \"Developers + AI agents\"\n\nWhen `multiSelect: true`, user can pick 2+ options. Results are joined with \" + \".\n\n## Score and instruction-file boundaries\n\nA 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\u002Foutput paths and preserve existing CLAUDE.md\u002FAGENTS.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.\n\n## Workflow\n\n### Step 1: Check Current State\n\nRun faf score to understand current position:\n\n```bash\nfaf score --verbose\n```\n\nOr get it as structured data for programmatic use:\n\n```bash\nfaf score --json\n```\n\n`--json` returns the score + per-slot breakdown — the empty slots are what you interview on (the priority order is in Step 2).\n\n### Step 2: Ask Questions Using AskUserQuestion\n\nFor each missing field, use Claude Code's AskUserQuestion tool:\n\n**Priority Order (most impactful first):**\n1. `project.goal` - What does this project do?\n2. `human_context.why` - Why does this exist?\n3. `human_context.who` - Who uses this?\n4. `human_context.what` - What problem does it solve?\n5. `project.main_language` - Primary language\n6. `stack.database` - Database choice\n7. `stack.hosting` - Where is it deployed?\n8. `stack.frontend` - Frontend framework\n9. `stack.backend` - Backend framework\n10. `human_context.where` - Environment\n11. `human_context.when` - Timeline\u002Fphase\n12. `human_context.how` - How the project is built (sourced from the stack)\n\n### Step 3: Apply Answers\n\nAfter collecting answers, update the .faf file:\n\n```bash\n# Read current .faf\ncat project.faf\n\n# Update fields (use Edit tool)\n# Then verify:\nfaf score\n```\n\n### Step 4: Celebrate or Continue\n\nIf score >= 100: Celebrate Gold Code achievement\nIf score \u003C 100: Continue with remaining questions\n\n## Question Templates for AskUserQuestion\n\n### Single-Select Questions (pick one)\n\n#### project.goal\n```json\n{\n  \"question\": \"What does this project do? (one clear sentence)\",\n  \"header\": \"Goal\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Let me type it\", \"description\": \"I'll describe it myself\"},\n    {\"label\": \"Help me write it\", \"description\": \"Guide me through it\"}\n  ]\n}\n```\n\n#### human_context.why\n```json\n{\n  \"question\": \"Why does this project exist?\",\n  \"header\": \"Why\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Business need\", \"description\": \"Solving a business problem\"},\n    {\"label\": \"Personal project\", \"description\": \"Learning or hobby\"},\n    {\"label\": \"Open source\", \"description\": \"Community contribution\"},\n    {\"label\": \"Let me explain\", \"description\": \"Custom reason\"}\n  ]\n}\n```\n\n#### stack.database\n```json\n{\n  \"question\": \"What database do you use?\",\n  \"header\": \"Database\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"PostgreSQL\", \"description\": \"Relational database\"},\n    {\"label\": \"MongoDB\", \"description\": \"Document database\"},\n    {\"label\": \"SQLite\", \"description\": \"File-based database\"},\n    {\"label\": \"None\", \"description\": \"No database\"}\n  ]\n}\n```\n\n#### stack.hosting\n```json\n{\n  \"question\": \"Where is this deployed?\",\n  \"header\": \"Hosting\",\n  \"multiSelect\": false,\n  \"options\": [\n    {\"label\": \"Vercel\", \"description\": \"Frontend\u002Fserverless\"},\n    {\"label\": \"AWS\", \"description\": \"Amazon Web Services\"},\n    {\"label\": \"Local only\", \"description\": \"Not deployed\"},\n    {\"label\": \"Other\", \"description\": \"Different platform\"}\n  ]\n}\n```\n\n### Multi-Select Questions (pick multiple, joined with \" + \")\n\n#### stack.testing\n```json\n{\n  \"question\": \"What testing tools\u002Fmethodologies do you use?\",\n  \"header\": \"Testing\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"pytest\", \"description\": \"Python testing framework\"},\n    {\"label\": \"Jest\", \"description\": \"JavaScript testing\"},\n    {\"label\": \"Vitest\", \"description\": \"Vite-native testing\"},\n    {\"label\": \"WJTTC\", \"description\": \"Championship methodology (Layer 2)\"}\n  ]\n}\n```\n**Result format:** `pytest + WJTTC` (industry first, WJTTC follows)\n\n**Ordering:** When both selected, industry tests come first:\n- `pytest + WJTTC` (not `WJTTC + pytest`)\n- WJTTC can also run standalone\n\n#### stack.cicd\n```json\n{\n  \"question\": \"What CI\u002FCD tools do you use?\",\n  \"header\": \"CI\u002FCD\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"GitHub Actions\", \"description\": \"GitHub-native CI\u002FCD\"},\n    {\"label\": \"Cloud Build\", \"description\": \"Google Cloud CI\u002FCD\"},\n    {\"label\": \"CircleCI\", \"description\": \"CircleCI pipelines\"},\n    {\"label\": \"None\", \"description\": \"No CI\u002FCD yet\"}\n  ]\n}\n```\n**Result format:** `GitHub Actions + Cloud Build`\n\n#### stack.frontend\n```json\n{\n  \"question\": \"What frontend technologies do you use?\",\n  \"header\": \"Frontend\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"React\", \"description\": \"React framework\"},\n    {\"label\": \"Next.js\", \"description\": \"React meta-framework\"},\n    {\"label\": \"Svelte\", \"description\": \"Svelte framework\"},\n    {\"label\": \"None\u002FAPI-only\", \"description\": \"No frontend\"}\n  ]\n}\n```\n\n#### human_context.who\n```json\n{\n  \"question\": \"Who uses this project?\",\n  \"header\": \"Users\",\n  \"multiSelect\": true,\n  \"options\": [\n    {\"label\": \"Developers\", \"description\": \"Software developers\"},\n    {\"label\": \"End users\", \"description\": \"Non-technical users\"},\n    {\"label\": \"AI agents\", \"description\": \"Claude, Gemini, etc.\"},\n    {\"label\": \"Internal team\", \"description\": \"Your team only\"}\n  ]\n}\n```\n**Result format:** `Developers + AI agents`\n\n### Processing Multi-Select Answers\n\nWhen user selects multiple options, join them with \" + \":\n\n```python\n# Example: User selects [\"pytest\", \"WJTTC\"]\nselected = [\"pytest\", \"WJTTC\"]\nvalue = \" + \".join(selected)  # \"pytest + WJTTC\"\n```\n\nThis creates readable, scannable values in the .faf file:\n```yaml\nstack:\n  testing: pytest + WJTTC\n  cicd: GitHub Actions + Cloud Build\n```\n\n## Example Session\n\n```\nUser: \u002Ffaf-go\n\nClaude: Let me check your current .faf status.\n\n[Runs: faf score --verbose]\n\nYour score is 45%. Let's get you to Gold Code!\n\n[Uses AskUserQuestion for project.goal]\n\nUser: [Selects option or types custom]\n\nClaude: Great! Now let's capture why this project exists.\n\n[Uses AskUserQuestion for human_context.why]\n\n... continues until 100% ...\n\nClaude: ✪ GOLD CODE ACHIEVED!\nAll active scoring slots are filled; accuracy and project coverage still need review.\n```\n\n## TodoWrite Integration\n\nTrack progress with todos:\n\n```javascript\n[\n  {\"content\": \"Answer project.goal question\", \"status\": \"completed\"},\n  {\"content\": \"Answer human_context.why question\", \"status\": \"in_progress\"},\n  {\"content\": \"Answer stack.database question\", \"status\": \"pending\"},\n  {\"content\": \"Verify Gold Code achieved\", \"status\": \"pending\"}\n]\n```\n\n## CLI Fallback\n\nOutside Claude Code, the same destination is reached with the CLI's own interactive interview:\n\n```bash\nfaf go            # interactive terminal interview (--resume continues a session)\n```\n\nThis skill is the **Claude-native** version of that interview — AskUserQuestion instead of terminal prompts. For structured, programmatic data, use `faf score --json`.\n\n## Success Metrics\n\n- User reaches 100% score\n- All required fields filled with meaningful content\n- No placeholder values (TBD, Unknown, None where inappropriate)\n- User understands what each field is for\n\n## On Completion\n\nWhen 100% ✪ is achieved:\n\n```\n✪ 100% — Gold Code\n\nproject.faf: complete\nCLAUDE.md:   synced from .faf\n```\n\nOptionally run `faf sync` to emit CLAUDE.md \u002F AGENTS.md from the .faf. Your AI now starts every session with complete project context.\n\n## Related Skills\n\n- **faf-context** — the builder's quickstart: hand the AI what it needs to hit 100%, fast\n- **faf-wizard** — done-for-you, one-click .faf for any project\n- **faf-expert** — master the format: scoring internals, MCP config, bi-sync, the full 21-slot model\n\n---\n\n> .faf is the format. project.faf is the file.\n> 100% ✪ AI-Readiness is the result.\n\n---\n\n*MIT · part of the FAF skill family (faf-context · faf-wizard · faf-expert). Native to Claude Code.*\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream source and local project context.\n- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.\n- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n",{"name":5,"risk":31,"source":50,"license":14,"date_added":51,"description":7,"source_repo":52,"source_type":36,"license_source":53},"https:\u002F\u002Fgithub.com\u002FWolfe-Jam\u002Ffaf-skills\u002Ftree\u002Fmain\u002Fskills\u002Ffaf-go",1782864000000,"Wolfe-Jam\u002Ffaf-skills","https:\u002F\u002Fgithub.com\u002FWolfe-Jam\u002Ffaf-skills\u002Fblob\u002Fmain\u002FLICENSE","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fantigravity-faf-go",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-11 14:20:43",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add boisenoise\u002Fskills-collections --skill faf-go",{"tool":66,"command":67},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill faf-go",{"tool":69,"command":70},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill faf-go","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-faf-go\u002FSKILL.md",true,[],[75,102,127,152,171,194],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":80,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":45},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,31,99,100,101,22,21],"web","access","web access","official","development","browser","search","data","Tools","read","fetch","only","youtube","free tools","platform","free","cover","normal",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":106,"sourceHtmlUrl":107,"categories":108,"keywords":114,"publishedAt":45},"01M6WNF9XAYAB2CZDDJBAKNM93","treg","Use the Treg CLI or MCP catalog to discover, price and call authorized external tools with team-scoped credentials and a user-approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[109],{"categoryNo":110,"categoryName":111,"taxonomyType":112,"taxonomyTypeDesc":113},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[104,115,116,44,87,88,42,92,117,118,119,120,37,121,122,31,123,124,125,126,21],"MCP","cli","Budget","credentials","team","catalog","call","discover","external tools","with","scoped","blob",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":131,"sourceHtmlUrl":132,"categories":133,"keywords":139,"publishedAt":45},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[134],{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[129,44,87,42,140,141,142,143,144,145,31,146,147,148,124,25,149,150,151,126,104,21],"https","email","enrichment","business","evidence","phone","ranking","post","job description","person","candidate","reason",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":156,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":45},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[160,161,162,44,87,42,163,164,140,165,117,166,167,168,144,31,169,170,126,104,21],"growth","rea","rea growth","produce","content","playbook","marketing","analyze","public","observations","company",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":175,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":45},"01M6WN2EVE3RQRGWAKBT4S7YT2","make-ugc","Create original AI UGC-style product videos through Treg: research hooks, approve a licensed presenter and voice, generate clips, add captions and report costs.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FSKILL.md",[],[179,180,44,181,87,182,42,183,184,185,186,187,31,188,189,190,191,192,193,126],"make","make ugc","research","create","Video","Captions","ads","short","hooks","head","style","niche","trending","talking-head","approve",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":79,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":201,"publishedAt":45},"01M6WN10ARAA63Y8K1YNGXJ2RM","lead-signals","Find and qualify public buying signals through Treg, rank fitting business accounts and optionally enrich authorized business contact information.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FSKILL.md",[],[202,203,204,44,87,42,205,206,143,207,208,31,209,210,211,212,213,214,215,126],"signals","lead","lead signals","how","find","ready","as","how to","qualify","people","information","enrich","changes","funding"]