[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70H0JZJVARDANNADF20ND0M":3,"skill-same:01M70H0JZJVARDANNADF20ND0M":80},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M70H0JZJVARDANNADF20ND0M","recsys-pipeline-architect",null,"Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks \"the top K items for a (user, context)\" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.","mdnaimul22\u002Fhuman-skills","mdnaimul22","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F64520358?v=4","01M70FH95MH2H796Q01TYSFZ03","Naimul","01M70FH96798CQR4YHA13F17GA",3,"MIT","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Frecsys-pipeline-architect\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"architect","recsys","recsys pipeline architect","pipeline","for","framework","context","source","rag","xai","top","filter","you","design","open","ranking","social","search","feeds","community","2026-11-10 10:12:30",false,"---\nname: recsys-pipeline-architect\ndescription: Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks \"the top K items for a (user, context)\" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.\nmetadata:\n  origin: community\n---\n\n# recsys-pipeline-architect\n\nA spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. It encodes the **six-stage pattern** — Source → Hydrator → Filter → Scorer → Selector → SideEffect — popularized by xAI's open-sourced [For You algorithm](https:\u002F\u002Fgithub.com\u002Fxai-org\u002Fx-algorithm) (Apache 2.0). This skill is an independent reimplementation of the pattern (MIT) — no code copied from the original.\n\nUpstream: \u003Chttps:\u002F\u002Fgithub.com\u002Fmturac\u002Frecsys-pipeline-architect>\n\n## When to Use\n\n- User wants to build any system that picks \"the top K items for a user\u002Fcontext\"\n- User asks \"how should I rank X\" or describes a feed\u002Fpersonalization problem\n- User has a scoring function and needs the pipeline plumbing around it\n- User wants to migrate from a single relevance score to multi-action prediction with tunable weights\n- User is wrapping an LLM\u002FML scorer and needs filters, hydrators, side-effects, and a runnable scaffold in their stack (TypeScript \u002F Go \u002F Python)\n- Triggers: \"recommendation system\", \"feed algorithm\", \"ranking pipeline\", \"for you feed\", \"candidate pipeline\", \"content recommender\", \"pipeline architecture for recsys\", \"RAG retrieval reranker\"\n\n## When NOT to Use\n\n- Model architecture work (transformer design, two-tower retrieval, embedding training) — this skill is plumbing *around* the model, not the model itself\n- Pure ML training pipelines — the scoring function is the user's responsibility\n- Operating a deployed pipeline (monitoring, autoscaling) — out of scope\n\n## The six-stage framework\n\n| # | Stage | Job | Parallel? |\n|---|---|---|---|\n| 1 | **Source** | Fetch candidates from one or more origins | Yes — multiple sources run in parallel |\n| 2 | **Hydrator** | Enrich each candidate with metadata needed for filtering and scoring | Yes — independent hydrators run in parallel |\n| 3 | **Filter** | Drop candidates that should never be shown (blocked, expired, duplicate, ineligible) | Sequential — each filter sees fewer items |\n| 4 | **Scorer** | Assign each surviving candidate one or more scores | Sequential — later scorers see earlier scores |\n| 5 | **Selector** | Sort by final score, return top K | Single op |\n| 6 | **SideEffect** | Cache served IDs, log impressions, emit events, update counters | Async — must never block the response |\n\n### Why this exact order\n\n- Sources before hydration: know what candidates exist before paying to enrich them\n- Hydration before filtering: many filters need metadata the source did not provide\n- Filtering before scoring: scoring is the expensive stage; drop the ineligible first\n- Scorer chain (not single scorer): real systems compose ML scoring + diversity reranking + business rules\n- Selector after scoring: keeps scoring deterministic and cacheable\n- SideEffects last and async: side effects must never block the user response\n\n## Workflow when invoked\n\nWalk the user through these eight steps:\n\n1. **Clarify the use case** (one round, three questions): items being ranked? input context? language\u002Fruntime?\n2. **Identify the candidate sources**: usually in-network (followed\u002Fowned\u002Fsubscribed) + out-of-network (ML retrieval \u002F trending \u002F similar-to-liked)\n3. **List required hydrations**: for each filter and scorer, what data does it need that the source did not provide?\n4. **List the filters**: duplicate, self, age, block\u002Fmute, previously-served, eligibility. Order matters — cheap before expensive.\n5. **Design the scorer chain**: primary (ML) → combiner (multi-action with weights) → diversity → business rules\n6. **Selector**: sort descending by final score, take top K (or stratified mix for in-network\u002Fout-of-network)\n7. **SideEffects**: cache served IDs, emit impression events, update counters, log analytics — all fire-and-forget\n8. **Generate the scaffold** in the user's stack\n\n## Key trade-offs to surface (don't default silently)\n\n### 1. Single score vs multi-action prediction\n\n- **Single score**: train one model to predict relevance. To change behavior → retrain.\n- **Multi-action**: predict `P(action)` for many actions (read, like, share, skip, report), combine with weights at serving time. To change behavior → change weights. No retraining.\n\nThe X For You system uses multi-action with both positive and negative weights. Recommend multi-action when the user expects to tune frequently.\n\n### 2. Candidate isolation in scoring\n\n- **Isolated**: each candidate scored independently. Deterministic, cacheable.\n- **Joint**: candidates attend to each other during scoring (e.g., transformer over batch). More expressive but non-deterministic across batches.\n\nDefault to isolation. Joint only when there's a specific reason (e.g., explicit batch-aware diversity).\n\n### 3. Online vs offline\n\n- **Request-time (online)**: pipeline runs on each request. Latency budget: 100–300ms. Default.\n- **Pre-computed (offline batch)**: pipeline runs periodically, results cached. Lower latency, lower freshness.\n- **Hybrid**: candidate retrieval offline, ranking online.\n\n## Hard rules\n\n1. **Do not invent benchmark numbers.** \"How much faster?\" → \"depends on workload, run it yourself.\"\n2. **Attribution discipline.** When the pattern is referenced, attribute as \"popularized by xAI's open-sourced For You algorithm\" \u002F `github.com\u002Fxai-org\u002Fx-algorithm` (Apache 2.0).\n3. **No trademark use.** Do not name the user's artifact \"X-like\" or use \"For You\" branding. Pattern is free; brand is not. Suggested naming: \"candidate pipeline\", \"feed pipeline\", \"ranking pipeline\", \"recsys pipeline\".\n4. **Surface trade-offs.** Multi-action vs single, isolation vs joint, online vs offline — never default silently.\n5. **The generated scaffold must run.** No pseudocode passing as code.\n6. **Filter order matters.** Cheap before expensive. Universal before user-specific.\n7. **Side effects never block.** Wrap in fire-and-forget patterns (goroutines \u002F promises without await \u002F asyncio tasks).\n\n## Anti-Patterns\n\n- Scoring before filtering (wastes compute on candidates that will be dropped anyway)\n- Synchronous side effects (cache writes \u002F impression emits blocking the response)\n- A single \"relevance\" score when the product needs to tune for multiple objectives (engagement vs safety vs diversity vs ads)\n- Joint scoring as default (non-deterministic, harder to cache, doesn't compose with reranking stages)\n- Generating pseudocode \"for illustration\" — the scaffold must actually run\n\n## Upstream contents\n\nThe upstream repository at \u003Chttps:\u002F\u002Fgithub.com\u002Fmturac\u002Frecsys-pipeline-architect> ships:\n\n- Full `SKILL.md` with the complete 8-step workflow\n- 5 load-on-demand reference docs: interfaces in 4 languages (TS\u002FGo\u002FPython\u002FRust), multi-action scoring pattern, candidate isolation, filter cookbook (12 patterns), scorer cookbook (weighted sum, MMR, diversity penalty, position debiasing)\n- 3 runnable example scaffolds, every one green on its test suite:\n  - Strapi v5 plugin (TypeScript \u002F Jest — 3\u002F3 pass)\n  - Zentra-compatible pipeline (Go with generics — 3\u002F3 pass)\n  - PMAI task prioritizer (Python \u002F FastAPI \u002F pytest — 3\u002F3 pass)\n- v0.1.0 release tagged\n- MIT license; pattern attributed to xAI X For You algorithm (Apache 2.0)\n\nInstall via skills.sh: `npx skills add mturac\u002Frecsys-pipeline-architect`\n","# recsys-pipeline-architect\n\nA spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. It encodes the **six-stage pattern** — Source → Hydrator → Filter → Scorer → Selector → SideEffect — popularized by xAI's open-sourced [For You algorithm](https:\u002F\u002Fgithub.com\u002Fxai-org\u002Fx-algorithm) (Apache 2.0). This skill is an independent reimplementation of the pattern (MIT) — no code copied from the original.\n\nUpstream: \u003Chttps:\u002F\u002Fgithub.com\u002Fmturac\u002Frecsys-pipeline-architect>\n\n## When to Use\n\n- User wants to build any system that picks \"the top K items for a user\u002Fcontext\"\n- User asks \"how should I rank X\" or describes a feed\u002Fpersonalization problem\n- User has a scoring function and needs the pipeline plumbing around it\n- User wants to migrate from a single relevance score to multi-action prediction with tunable weights\n- User is wrapping an LLM\u002FML scorer and needs filters, hydrators, side-effects, and a runnable scaffold in their stack (TypeScript \u002F Go \u002F Python)\n- Triggers: \"recommendation system\", \"feed algorithm\", \"ranking pipeline\", \"for you feed\", \"candidate pipeline\", \"content recommender\", \"pipeline architecture for recsys\", \"RAG retrieval reranker\"\n\n## When NOT to Use\n\n- Model architecture work (transformer design, two-tower retrieval, embedding training) — this skill is plumbing *around* the model, not the model itself\n- Pure ML training pipelines — the scoring function is the user's responsibility\n- Operating a deployed pipeline (monitoring, autoscaling) — out of scope\n\n## The six-stage framework\n\n| # | Stage | Job | Parallel? |\n|---|---|---|---|\n| 1 | **Source** | Fetch candidates from one or more origins | Yes — multiple sources run in parallel |\n| 2 | **Hydrator** | Enrich each candidate with metadata needed for filtering and scoring | Yes — independent hydrators run in parallel |\n| 3 | **Filter** | Drop candidates that should never be shown (blocked, expired, duplicate, ineligible) | Sequential — each filter sees fewer items |\n| 4 | **Scorer** | Assign each surviving candidate one or more scores | Sequential — later scorers see earlier scores |\n| 5 | **Selector** | Sort by final score, return top K | Single op |\n| 6 | **SideEffect** | Cache served IDs, log impressions, emit events, update counters | Async — must never block the response |\n\n### Why this exact order\n\n- Sources before hydration: know what candidates exist before paying to enrich them\n- Hydration before filtering: many filters need metadata the source did not provide\n- Filtering before scoring: scoring is the expensive stage; drop the ineligible first\n- Scorer chain (not single scorer): real systems compose ML scoring + diversity reranking + business rules\n- Selector after scoring: keeps scoring deterministic and cacheable\n- SideEffects last and async: side effects must never block the user response\n\n## Workflow when invoked\n\nWalk the user through these eight steps:\n\n1. **Clarify the use case** (one round, three questions): items being ranked? input context? language\u002Fruntime?\n2. **Identify the candidate sources**: usually in-network (followed\u002Fowned\u002Fsubscribed) + out-of-network (ML retrieval \u002F trending \u002F similar-to-liked)\n3. **List required hydrations**: for each filter and scorer, what data does it need that the source did not provide?\n4. **List the filters**: duplicate, self, age, block\u002Fmute, previously-served, eligibility. Order matters — cheap before expensive.\n5. **Design the scorer chain**: primary (ML) → combiner (multi-action with weights) → diversity → business rules\n6. **Selector**: sort descending by final score, take top K (or stratified mix for in-network\u002Fout-of-network)\n7. **SideEffects**: cache served IDs, emit impression events, update counters, log analytics — all fire-and-forget\n8. **Generate the scaffold** in the user's stack\n\n## Key trade-offs to surface (don't default silently)\n\n### 1. Single score vs multi-action prediction\n\n- **Single score**: train one model to predict relevance. To change behavior → retrain.\n- **Multi-action**: predict `P(action)` for many actions (read, like, share, skip, report), combine with weights at serving time. To change behavior → change weights. No retraining.\n\nThe X For You system uses multi-action with both positive and negative weights. Recommend multi-action when the user expects to tune frequently.\n\n### 2. Candidate isolation in scoring\n\n- **Isolated**: each candidate scored independently. Deterministic, cacheable.\n- **Joint**: candidates attend to each other during scoring (e.g., transformer over batch). More expressive but non-deterministic across batches.\n\nDefault to isolation. Joint only when there's a specific reason (e.g., explicit batch-aware diversity).\n\n### 3. Online vs offline\n\n- **Request-time (online)**: pipeline runs on each request. Latency budget: 100–300ms. Default.\n- **Pre-computed (offline batch)**: pipeline runs periodically, results cached. Lower latency, lower freshness.\n- **Hybrid**: candidate retrieval offline, ranking online.\n\n## Hard rules\n\n1. **Do not invent benchmark numbers.** \"How much faster?\" → \"depends on workload, run it yourself.\"\n2. **Attribution discipline.** When the pattern is referenced, attribute as \"popularized by xAI's open-sourced For You algorithm\" \u002F `github.com\u002Fxai-org\u002Fx-algorithm` (Apache 2.0).\n3. **No trademark use.** Do not name the user's artifact \"X-like\" or use \"For You\" branding. Pattern is free; brand is not. Suggested naming: \"candidate pipeline\", \"feed pipeline\", \"ranking pipeline\", \"recsys pipeline\".\n4. **Surface trade-offs.** Multi-action vs single, isolation vs joint, online vs offline — never default silently.\n5. **The generated scaffold must run.** No pseudocode passing as code.\n6. **Filter order matters.** Cheap before expensive. Universal before user-specific.\n7. **Side effects never block.** Wrap in fire-and-forget patterns (goroutines \u002F promises without await \u002F asyncio tasks).\n\n## Anti-Patterns\n\n- Scoring before filtering (wastes compute on candidates that will be dropped anyway)\n- Synchronous side effects (cache writes \u002F impression emits blocking the response)\n- A single \"relevance\" score when the product needs to tune for multiple objectives (engagement vs safety vs diversity vs ads)\n- Joint scoring as default (non-deterministic, harder to cache, doesn't compose with reranking stages)\n- Generating pseudocode \"for illustration\" — the scaffold must actually run\n\n## Upstream contents\n\nThe upstream repository at \u003Chttps:\u002F\u002Fgithub.com\u002Fmturac\u002Frecsys-pipeline-architect> ships:\n\n- Full `SKILL.md` with the complete 8-step workflow\n- 5 load-on-demand reference docs: interfaces in 4 languages (TS\u002FGo\u002FPython\u002FRust), multi-action scoring pattern, candidate isolation, filter cookbook (12 patterns), scorer cookbook (weighted sum, MMR, diversity penalty, position debiasing)\n- 3 runnable example scaffolds, every one green on its test suite:\n  - Strapi v5 plugin (TypeScript \u002F Jest — 3\u002F3 pass)\n  - Zentra-compatible pipeline (Go with generics — 3\u002F3 pass)\n  - PMAI task prioritizer (Python \u002F FastAPI \u002F pytest — 3\u002F3 pass)\n- v0.1.0 release tagged\n- MIT license; pattern attributed to xAI X For You algorithm (Apache 2.0)\n\nInstall via skills.sh: `npx skills add mturac\u002Frecsys-pipeline-architect`\n",{"name":5,"metadata":57,"description":7},{"origin":51},"main","74715deacdc69119d739f38916f8ee4f0f979ebf","skills\u002Fstorage\u002Faffaan-m_ECC\u002Frecsys-pipeline-architect",0,"https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills","https:\u002F\u002Fgithub.com\u002Fmdnaimul22","AI Agents Workflows Automation | Specializing in Python, FastAPI, MCP Agents & Custom ERP.","2026-11-11 21:16:57",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add mdnaimul22\u002Fhuman-skills --skill recsys-pipeline-architect",{"tool":72,"command":73},"bunx","bunx skills add mdnaimul22\u002Fhuman-skills --skill recsys-pipeline-architect",{"tool":75,"command":76},"pnpm","pnpm dlx skills add mdnaimul22\u002Fhuman-skills --skill recsys-pipeline-architect","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Frecsys-pipeline-architect\u002FSKILL.md",true,[],[81,89,115,135,154,169],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":88},"01M72JT3YP1SQHD0MANGDXQ4RT","higgsfield-websites","Build, edit, and deploy full-stack websites, apps and games via the Higgsfield CLI (`higgsfield website …`). Each is a React 19 + TanStack Start SSR app in one Cloudflare Worker (D1\u002FR2\u002FKV\u002FDO\u002FContainers). THREE product types, picked via `--type` on create: `website` (standalone, no Higgsfield integration — references\u002Fwebsite-flow.md), `app` (Sign in with Higgsfield + fnf SDK, Quanta — references\u002Fapp-flow.md), `game` (realtime multiplayer rooms — references\u002Fgame-flow.md). Routes to the right flow; each carries its own rules and deploy\u002Fpublish gates.\nUse when: \"build me a website\", \"make a landing page\", \"create a web app\", \"build a SaaS dashboard \u002F portfolio\", \"make me a game\", \"deploy this site\", \"publish\". Also owns GAME ART: \"make a spritesheet\", \"tileable texture\", \"animate a 3D character\", game music\u002FSFX — see the game-* references.\nNOT for: single image\u002Fvideo\u002Faudio generation (higgsfield-generate), product photos (higgsfield-product-photoshoot), marketplace cards (higgsfield-marketplace-cards).","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fhiggsfield-ai_skills\u002Fhiggsfield-websites\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":105,"publishedAt":52},"01M70JPK8HAXXA85P7Q8NKZZYG","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-skills\u002FSKILL.md",[95,98,101,102],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":99,"categoryName":100,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":103,"categoryName":104,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[106,107,108,109,110,111,112,113,114],"writing","writing skills","editing","deployment","use","Existing","new","when","work",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":126,"publishedAt":52},"01M70JP4PHBH1CQVVDWZ94W159","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-plans\u002FSKILL.md",[121,122,123],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":99,"categoryName":100,"taxonomyType":29,"taxonomyTypeDesc":30},{"categoryNo":124,"categoryName":125,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师",[106,127,128,44,113,129,36,110,130,131,132,133,134],"writing plans","plans","code","requirements","task","multi","spec","multi-step task",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":52},"01M70JNTYVGXFZT8PASZ844TH8","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fverification-before-completion\u002FSKILL.md",[],[142,143,144,145,146,147,110,148,149,150,113,151,114,152,153],"verification","verification before completion","complete","about","output","assertions","prs","evidence","claim","claims","commands","running",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":161,"publishedAt":52},"01M70JNGVSQDESWN0Q5BPJ875V","using-superpowers","Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-superpowers\u002FSKILL.md",[160],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[162,163,164,165,110,113,166,167,168],"using superpowers","superpowers","conversation","how to","how","find","questions",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":52},"01M70JN57CE588NPYPYTWH1DWG","using-git-worktrees","Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-git-worktrees\u002FSKILL.md",[],[176,177,178,179,180,181,113,114,182,183,184,185,186,187,188,128,110,189,190],"using git worktrees","Using git","git","worktrees","git worktrees","implementation","git worktree","worktree","native","Tools","isolation","fallback","feature","via","workspace"]