[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE1QSWH641AAN25SQQ8MW7":3,"skill-same:01M6AE1QSWH641AAN25SQQ8MW7":72},{"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":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6AE1QSWH641AAN25SQQ8MW7","qdrant-performance-optimization",null,"Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41162,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fqdrant-performance-optimization\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"Optimization","Qdrant","qdrant performance optimization","performance optimization","performance","query optimization","when","techniques","you","indexing","speed","strategies","efficiency","query","optimize","use","read","grep","improve","deployment","2026-11-10 10:12:30",false,"---\nname: qdrant-performance-optimization\ndescription: \"Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.\"\nallowed-tools:\n  - Read\n  - Grep\n  - Glob\n---\n\n\n# Qdrant Performance Optimization\n\nThere are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.\n\n\n## Search Speed Optimization\n\nThere are two different criteria for search speed: latency and throughput. \nLatency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame.\nDepending on your use case, you may want to optimize for one or both of these metrics.\n\nMore on search speed optimization can be found in the [Search Speed Optimization](search-speed-optimization\u002FSKILL.md) skill.\n\n\n## Indexing Performance Optimization\n\nQdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.\n\nMore on indexing performance optimization can be found in the [Indexing Performance Optimization](indexing-performance-optimization\u002FSKILL.md) skill.\n\n\n## Memory Usage Optimization\n\nVector search can be memory intensive, especially when dealing with large datasets.\nQdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.\n\nMore on memory usage optimization can be found in the [Memory Usage Optimization](memory-usage-optimization\u002FSKILL.md) skill.","# Qdrant Performance Optimization\n\nThere are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.\n\n\n## Search Speed Optimization\n\nThere are two different criteria for search speed: latency and throughput. \nLatency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame.\nDepending on your use case, you may want to optimize for one or both of these metrics.\n\nMore on search speed optimization can be found in the [Search Speed Optimization](search-speed-optimization\u002FSKILL.md) skill.\n\n\n## Indexing Performance Optimization\n\nQdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.\n\nMore on indexing performance optimization can be found in the [Indexing Performance Optimization](indexing-performance-optimization\u002FSKILL.md) skill.\n\n\n## Memory Usage Optimization\n\nVector search can be memory intensive, especially when dealing with large datasets.\nQdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.\n\nMore on memory usage optimization can be found in the [Memory Usage Optimization](memory-usage-optimization\u002FSKILL.md) skill.",{"name":5,"description":7,"allowed-tools":44},[45,46,47],"Read","Grep","Glob","main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fqdrant-performance-optimization",5360,"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.","2026-11-15 04:42:30",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add github\u002Fawesome-copilot --skill qdrant-performance-optimization",{"tool":64,"command":65},"bunx","bunx skills add github\u002Fawesome-copilot --skill qdrant-performance-optimization",{"tool":67,"command":68},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill qdrant-performance-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fqdrant-performance-optimization\u002FSKILL.md",true,[],[73,105,133,162,196,213],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":87,"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",[79,84],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000023","Skill与智能体开发",[88,89,90,91,92,25,93,94,35,95,96,97,98,99,100,101,34,102,103,104],"supabase","rls","supabase rls","supabase rls policies","project","review","change","write","security","isolation","tables","as","tests","prove","users","functions","with",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":112,"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",[111],{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},[113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":137,"sourceHtmlUrl":138,"categories":139,"keywords":145,"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",[140],{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[146,147,148,149,150,151,119,92,38,152,153,154,155,156,157,34,158,159,160,161],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":166,"sourceHtmlUrl":167,"categories":168,"keywords":173,"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",[169,172],{"categoryNo":170,"categoryName":171,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,25,189,190,191,192,193,119,194,195],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":200,"licenseFileUrl":201,"sourceHtmlUrl":202,"categories":203,"keywords":209,"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",[204,205,208],{"categoryNo":170,"categoryName":171,"taxonomyType":82,"taxonomyTypeDesc":83},{"categoryNo":206,"categoryName":207,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},[210,116,183,211,212],"resemble detect","https","identity",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":200,"licenseFileUrl":217,"sourceHtmlUrl":218,"categories":219,"keywords":224,"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",[220,223],{"categoryNo":221,"categoryName":222,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":85,"categoryName":86,"taxonomyType":82,"taxonomyTypeDesc":83},[225,226,227,228,147,93,9,115,229,230,211,231,232,233,234,235,236,237,238,239,240,241,100,242,243,244,245],"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"]