[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE2VJR8PVHAECDG6NFEPYQ":3,"skill-same:01M6AE2VJR8PVHAECDG6NFEPYQ":68},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6AE2VJR8PVHAECDG6NFEPYQ","qdrant-search-strategies",null,"Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', 'ColBERT reranking', or 'missing keyword matches'","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41046,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fqdrant-search-quality\u002Fsearch-strategies\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"Qdrant","qdrant search strategies","search","strategies","when","relevance feedback","my","re","relevant","Hybrid Search","guides","search strategy","how to","bm25","dataset","get","selection","good","how","api","2026-11-10 10:12:30",false,"---\nname: qdrant-search-strategies\ndescription: \"Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', 'ColBERT reranking', or 'missing keyword matches'\"\n---\n\n# How to Improve Search Results with Advanced Strategies\n\nThese strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first.\nIf the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality.\n\n## Missing Obvious Keyword Matches\n\nUse when: pure vector search misses results that contain obvious keyword matches. Domain terminology not in embedding training data, exact keyword matching critical (brand names, SKUs), acronyms common. Skip when: pure semantic queries, all data in training set, latency budget very tight.\n\n- Dense + sparse with `prefetch` and fusion [Hybrid search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=hybrid-search)\n- Prefer learned sparse ([miniCOIL](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ffastembed\u002Ffastembed-minicoil\u002F), SPLADE, GTE) over raw BM25 if applicable (when user needs smart keywords matching and learned sparse models know the vocabulary of the domain)\n- For non-English languages, [configure sparse BM25 parameters accordingly](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Ftext-search\u002F?s=language-specific-settings)\n- RRF: good default, supports weighted (v1.17+) [RRF](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=reciprocal-rank-fusion-rrf)\n- DBSF with asymmetric limits (sparse_limit=250, dense_limit=100) can outperform RRF for technical docs [DBSF](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=distribution-based-score-fusion-dbsf)\n- Fusion can also be done through reranking\n\n## Right Documents Found But Wrong Order\n\nUse when: good recall but poor precision (right docs in top-100, not top-10).\n\n- Cross-encoder rerankers via FastEmbed [Rerankers](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ffastembed\u002Ffastembed-rerankers\u002F)\n- See how to use [Multistage queries](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=multi-stage-queries) in Qdrant\n- ColBERT and ColPali\u002FColQwen reranking is especially precise due to late interaction mechanisms, but it is heavy. It is important to configure and store multivectors without building HNSW for them to save resources. See [Multivector representation](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-search-engineering\u002Fusing-multivector-representations\u002F)\n\n## Right Documents Not Found But They Are There\n\nUse when: basic retrieval is in place but the retriever misses relevant items you know exist in the dataset. Works on any embeddable data (text, images, etc.).\n\nRelevance Feedback (RF) Query uses a feedback model's scores on retrieved results to steer the retriever through the full vector space on subsequent iterations, like reranking the entire collection through the retriever. Complementary to reranking: a reranker sees a limited subset, RF leverages feedback signals collection-wide. Even 3–5 feedback scores are enough. Can run multiple iterations.\n\nA feedback model is anything producing a relevance score per document: a bi-encoder, cross-encoder, late-interaction model, LLM-as-judge. Fuzzy relevance scores work, not just binary (good\u002Fbad, relevant\u002Firrelevant), due to the fact that feedback is expressed as a graded relevance score (higher = more relevant).\n\nSkip when: if the retriever already has strong recall, or if retriever and feedback model strongly agree on relevance.\n\n- RF Query is currently based on a [3-parameter naive formula](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=naive-strategy) with no universal defaults, so it must be tuned per dataset, retriever, and feedback model\n- Use [qdrant-relevance-feedback](https:\u002F\u002Fpypi.org\u002Fproject\u002Fqdrant-relevance-feedback\u002F) to tune parameters, evaluate impact with Evaluator, and check retriever-feedback agreement. See README for setup instructions. No GPUs are needed, and the framework also provides predefined retriever and feedback model options.\n- Check the configuration of the [Relevance Feedback Query API](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=relevance-feedback)\n- Use this as a helper end-to-end text retrieval example with parameter tuning and evals to understand how to use the API and run the `qdrant-relevance-feedback` framework: [RF tutorial](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-search-engineering\u002Fusing-relevance-feedback\u002F)\n\n## Results Too Similar\n\nUse when: top results are redundant, near-duplicates, or lack diversity. Common in dense content domains (academic papers, product catalogs).\n\n- Use MMR (v1.15+) as a query parameter with `diversity` to balance relevance and diversity [MMR](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=maximal-marginal-relevance-mmr)\n- Start with `diversity=0.5`, lower for more precision, higher for more exploration\n- MMR is slower than standard search. Only use when redundancy is an actual problem.\n\n## Know What Good Results Could Look Like But Can't Get Them\n\nUse when: you can provide positive and negative example points to steer search closer to positive and further from negative.\n\n- Recommendation API: positive\u002Fnegative examples to recommend fitting vectors [Recommendation API](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=recommendation-api)\n  - Best score strategy: better for diverse examples, supports negative-only [Best score](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=best-score-strategy)\n- Discovery API: context pairs (positive\u002Fnegative) to constrain search regions without a request target [Discovery](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=discovery-api)\n\n## Have Business Logic Behind Relevance\nUse when: results should be additionally ranked according to some business logic based on data, like recency or distance.\n\nCheck how to set up in [Score Boosting docs](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=score-boosting)\n\n## What NOT to Do\n\n- Use hybrid search before verifying pure vector quality (adds complexity, may mask model issues)\n- Use BM25 on non-English text without correctly configuring language-specific stop-word removal (severely degraded results)\n- Skip evaluation when adding relevance feedback (it's good to check on real queries that it actually could help)\n","# How to Improve Search Results with Advanced Strategies\n\nThese strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first.\nIf the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality.\n\n## Missing Obvious Keyword Matches\n\nUse when: pure vector search misses results that contain obvious keyword matches. Domain terminology not in embedding training data, exact keyword matching critical (brand names, SKUs), acronyms common. Skip when: pure semantic queries, all data in training set, latency budget very tight.\n\n- Dense + sparse with `prefetch` and fusion [Hybrid search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=hybrid-search)\n- Prefer learned sparse ([miniCOIL](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ffastembed\u002Ffastembed-minicoil\u002F), SPLADE, GTE) over raw BM25 if applicable (when user needs smart keywords matching and learned sparse models know the vocabulary of the domain)\n- For non-English languages, [configure sparse BM25 parameters accordingly](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Ftext-search\u002F?s=language-specific-settings)\n- RRF: good default, supports weighted (v1.17+) [RRF](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=reciprocal-rank-fusion-rrf)\n- DBSF with asymmetric limits (sparse_limit=250, dense_limit=100) can outperform RRF for technical docs [DBSF](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=distribution-based-score-fusion-dbsf)\n- Fusion can also be done through reranking\n\n## Right Documents Found But Wrong Order\n\nUse when: good recall but poor precision (right docs in top-100, not top-10).\n\n- Cross-encoder rerankers via FastEmbed [Rerankers](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ffastembed\u002Ffastembed-rerankers\u002F)\n- See how to use [Multistage queries](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fhybrid-queries\u002F?s=multi-stage-queries) in Qdrant\n- ColBERT and ColPali\u002FColQwen reranking is especially precise due to late interaction mechanisms, but it is heavy. It is important to configure and store multivectors without building HNSW for them to save resources. See [Multivector representation](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-search-engineering\u002Fusing-multivector-representations\u002F)\n\n## Right Documents Not Found But They Are There\n\nUse when: basic retrieval is in place but the retriever misses relevant items you know exist in the dataset. Works on any embeddable data (text, images, etc.).\n\nRelevance Feedback (RF) Query uses a feedback model's scores on retrieved results to steer the retriever through the full vector space on subsequent iterations, like reranking the entire collection through the retriever. Complementary to reranking: a reranker sees a limited subset, RF leverages feedback signals collection-wide. Even 3–5 feedback scores are enough. Can run multiple iterations.\n\nA feedback model is anything producing a relevance score per document: a bi-encoder, cross-encoder, late-interaction model, LLM-as-judge. Fuzzy relevance scores work, not just binary (good\u002Fbad, relevant\u002Firrelevant), due to the fact that feedback is expressed as a graded relevance score (higher = more relevant).\n\nSkip when: if the retriever already has strong recall, or if retriever and feedback model strongly agree on relevance.\n\n- RF Query is currently based on a [3-parameter naive formula](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=naive-strategy) with no universal defaults, so it must be tuned per dataset, retriever, and feedback model\n- Use [qdrant-relevance-feedback](https:\u002F\u002Fpypi.org\u002Fproject\u002Fqdrant-relevance-feedback\u002F) to tune parameters, evaluate impact with Evaluator, and check retriever-feedback agreement. See README for setup instructions. No GPUs are needed, and the framework also provides predefined retriever and feedback model options.\n- Check the configuration of the [Relevance Feedback Query API](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=relevance-feedback)\n- Use this as a helper end-to-end text retrieval example with parameter tuning and evals to understand how to use the API and run the `qdrant-relevance-feedback` framework: [RF tutorial](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-search-engineering\u002Fusing-relevance-feedback\u002F)\n\n## Results Too Similar\n\nUse when: top results are redundant, near-duplicates, or lack diversity. Common in dense content domains (academic papers, product catalogs).\n\n- Use MMR (v1.15+) as a query parameter with `diversity` to balance relevance and diversity [MMR](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=maximal-marginal-relevance-mmr)\n- Start with `diversity=0.5`, lower for more precision, higher for more exploration\n- MMR is slower than standard search. Only use when redundancy is an actual problem.\n\n## Know What Good Results Could Look Like But Can't Get Them\n\nUse when: you can provide positive and negative example points to steer search closer to positive and further from negative.\n\n- Recommendation API: positive\u002Fnegative examples to recommend fitting vectors [Recommendation API](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=recommendation-api)\n  - Best score strategy: better for diverse examples, supports negative-only [Best score](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=best-score-strategy)\n- Discovery API: context pairs (positive\u002Fnegative) to constrain search regions without a request target [Discovery](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fexplore\u002F?s=discovery-api)\n\n## Have Business Logic Behind Relevance\nUse when: results should be additionally ranked according to some business logic based on data, like recency or distance.\n\nCheck how to set up in [Score Boosting docs](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch-relevance\u002F?s=score-boosting)\n\n## What NOT to Do\n\n- Use hybrid search before verifying pure vector quality (adds complexity, may mask model issues)\n- Use BM25 on non-English text without correctly configuring language-specific stop-word removal (severely degraded results)\n- Skip evaluation when adding relevance feedback (it's good to check on real queries that it actually could help)\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fqdrant-search-quality\u002Fsearch-strategies",5340,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-11 13:41:32",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add github\u002Fawesome-copilot --skill qdrant-search-strategies",{"tool":60,"command":61},"bunx","bunx skills add github\u002Fawesome-copilot --skill qdrant-search-strategies",{"tool":63,"command":64},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill qdrant-search-strategies","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fqdrant-search-quality\u002Fsearch-strategies\u002FSKILL.md",true,[],[69,103,131,161,193,210],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":83,"publishedAt":39},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[75,80],{"categoryNo":76,"categoryName":77,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":81,"categoryName":82,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000023","Skill与智能体开发",[84,85,86,87,88,23,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"supabase","rls","supabase rls","supabase rls policies","project","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions","with",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":110,"publishedAt":39},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[109],{"categoryNo":81,"categoryName":82,"taxonomyType":78,"taxonomyTypeDesc":79},[111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":135,"sourceHtmlUrl":136,"categories":137,"keywords":143,"publishedAt":39},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[138],{"categoryNo":139,"categoryName":140,"taxonomyType":141,"taxonomyTypeDesc":142},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[144,145,146,147,148,149,117,88,150,151,152,153,154,155,156,99,157,158,159,160],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":165,"sourceHtmlUrl":166,"categories":167,"keywords":172,"publishedAt":39},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[168,171],{"categoryNo":169,"categoryName":170,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":139,"categoryName":140,"taxonomyType":141,"taxonomyTypeDesc":142},[173,21,38,174,175,176,177,178,179,180,181,182,183,184,185,23,186,187,188,189,190,117,191,192],"winmd api search","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":206,"publishedAt":39},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[201,202,205],{"categoryNo":169,"categoryName":170,"taxonomyType":78,"taxonomyTypeDesc":79},{"categoryNo":203,"categoryName":204,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":81,"categoryName":82,"taxonomyType":78,"taxonomyTypeDesc":79},[207,114,180,208,209],"resemble detect","https","identity",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":214,"sourceHtmlUrl":215,"categories":216,"keywords":221,"publishedAt":39},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[217,220],{"categoryNo":218,"categoryName":219,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":81,"categoryName":82,"taxonomyType":78,"taxonomyTypeDesc":79},[222,223,224,225,145,89,9,113,226,227,208,228,229,230,231,232,233,234,235,236,237,238,97,239,240,241,242],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]