[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JDFB2P8FD9W9EFZCS0623H":3,"skill-same:01M6JDFB2P8FD9W9EFZCS0623H":70},{"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":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6JDFB2P8FD9W9EFZCS0623H","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.","S3YED\u002Fappie-kit","S3YED","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F62743867?v=4","01M6JBKBRRVP6KMS0VWZ27T3R6","01M6JBKBX0KG30284PW1DBYJT1",14,"MIT","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Frecsys-pipeline-architect\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"pipeline","recsys pipeline architect","architect","recsys","community","source","for","top","you","filter","ranking","social","design","context","xai","open","feeds","search","framework","rag","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.\norigin: 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,"origin":27,"description":7},"master","8ec37df6d71fb664ea19d32c079e7d3ff931a8e0","skills\u002Fecc\u002Frecsys-pipeline-architect",3,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit","https:\u002F\u002Fweblyfe.ai","https:\u002F\u002Fgithub.com\u002FS3YED","2026-08-27 01:54:02",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add S3YED\u002Fappie-kit --skill recsys-pipeline-architect",{"tool":62,"command":63},"bunx","bunx skills add S3YED\u002Fappie-kit --skill recsys-pipeline-architect",{"tool":65,"command":66},"pnpm","pnpm dlx skills add S3YED\u002Fappie-kit --skill recsys-pipeline-architect","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Frecsys-pipeline-architect\u002FSKILL.md",true,[],[71,103,137,160,191,215],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":84,"publishedAt":43},"01M6JFD4M6N4DSGC7TXEJ5R3J2","ai-music","AI music generation and audio tools: HeartMuLa lyrics-to-music generation, songwriting craft (+ Suno prompts), and audio spectrogram\u002Ffeature visualization.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fwethlete\u002Fai-music\u002FSKILL.md",[77,80,83],{"categoryNo":78,"categoryName":79,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":81,"categoryName":82,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"music","ai music","humanizer","visualization","Tools","Audio","hermes","hermes agent","songwriting","suno","lyrics","prompts","heartmula","craft","generation","feature","ai music generation","music generation",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":117,"publishedAt":43},"01M6JFCZE4XNFQFMNZDHKHT5V5","agentic-webdesign-design-system-ingestion","Use v0 Design Systems 2.0, Bolt Design System Agents, or Lovable to generate production UI from a client's real design system components — not generic Tailwind. Use when building a client website where brand consistency matters.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fweb-design\u002Fagentic-webdesign-design-system-ingestion\u002FSKILL.md",[109,112],{"categoryNo":110,"categoryName":111,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[35,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"agentic","design system","system","ingestion","webdesign","agentic webdesign design system ingestion","components","generate","design systems","website","ui","Tailwind","bolt","lovable","v0","design-system","wed","design system components","appie",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":145,"publishedAt":43},"01M6JFCVPX3913CFAS6QQJ33ZB","agentic-webdesign-agent-elements","Use 21st.dev Agent Elements — 25 shadcn\u002Fui components purpose-built for AI agent UIs. Tool cards (Bash, Edit, Search, Plan, Subagent, MCP), chat, input, streaming. Open source, shadcn-registry install, no runtime lock-in.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fweb-design\u002Fagentic-webdesign-agent-elements\u002FSKILL.md",[143,144],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":113,"categoryName":114,"taxonomyType":115,"taxonomyTypeDesc":116},[118,146,122,147,148,149,124,150,40,151,152,38,153,154,155,156,157,158,159,28],"elements","agentic webdesign agent elements","MCP","ai agent","registry","streaming","open source","Bash","plan","subagent","edit","install","input","lock",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":170,"publishedAt":43},"01M6JFCQC7H65M9QJ6TRE71BS1","vercel-deployment","Deploy static sites and frontends to Vercel — handles both CLI and REST API paths, including the CLI token sanitization workaround.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Fvercel-deployment\u002FSKILL.md",[166,169],{"categoryNo":167,"categoryName":168,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190],"deployment","vercel deployment","vercel","macos","cli","linux","windows","deploy","vercel deploy","REST","api","site","deploy to vercel","token","rest api","host","sites","paths","sanitization","static site",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":43},"01M6JFCM4SMTXBM5A01BK2YQG1","software-quality-workflows","Umbrella workflow for TDD, debugging, code simplification, spikes, pre-commit review, and debugger-assisted investigation.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Fsoftware-quality-workflows\u002FSKILL.md",[197],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,29],"workflows","quality","software","software quality workflows","review","tdd","workflow","commit","debugging","code","debugger","pre-commit","investigation","code simplification","user","simplification",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":228,"publishedAt":43},"01M6JFCGD0M6CEATY01W7YN6DB","react-component-refactoring","Safely refactor large React components into smaller, maintainable pieces using proven patterns","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Freact-component-refactoring\u002FSKILL.md",[221,224,227],{"categoryNo":222,"categoryName":223,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":225,"categoryName":226,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[229,230,231,232,233,234,235,236,174,237,238,124,239,177,240,208,241,242,243,244],"react","component","refactoring","component refactoring","react component","react component refactoring","frontend","writing","safe","typescript","react components","modular","refactor","simplify","patterns","test-driven-development"]