[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE2HKPCS8AKS4RFM8R75H3":3,"skill-same:01M6AE2HKPCS8AKS4RFM8R75H3":71},{"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":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":46,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M6AE2HKPCS8AKS4RFM8R75H3","qdrant-scaling-qps",null,"Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.","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-scaling\u002Fscaling-qps\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"Qdrant","qdrant scaling","qdrant scaling qps","scaling","queries","when","second","more","throughput","guides","how to","query","search","use","read","how","batch","2026-11-10 10:12:30",false,"---\nname: qdrant-scaling-qps\ndescription: \"Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.\"\n---\n\n# Scaling for Query Throughput (QPS)\n\nThroughput scaling means handling more parallel queries per second. \nThis is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node.\n\nHigh throughput favors fewer, larger segments so each query touches less overhead.\n\n\n## Performance Tuning for Higher RPS\n\n- Use fewer, larger segments (`default_segment_number: 2`) [Maximizing throughput](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Foptimize\u002F?s=maximizing-throughput)\n- Enable quantization with `always_ram=true` to reduce disk IO [Quantization](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F)\n- Use batch search API to amortize overhead [Batch search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch\u002F?s=batch-search-api)\n\n## Minimize impact of Update Workloads\n\n- Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads [Low latency search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F)\n- Set `optimizer_cpu_budget` to limit indexing CPUs (e.g. `2` on an 8-CPU node reserves 6 for queries)\n- Configure delayed read fan-out (v1.17+) for tail latency [Delayed fan-outs](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F?s=use-delayed-fan-outs)\n\n\n\n## Horizontal Scaling for Throughput\n\nIf a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.\n\n- Shard replicas serve queries from replicated shards, distributing read load across nodes\n- Each replica adds independent query capacity without re-sharding\n- Use `replication_factor: 2+` and route reads to replicas [Distributed deployment](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fdistributed_deployment\u002F?s=replication)\n\nSee also [Horizontal Scaling](..\u002Fscaling-data-volume\u002Fhorizontal-scaling\u002FSKILL.md) for general horizontal scaling guidance.\n\n\n## Disk I\u002FO Bottlenecks\n\nIf it is not possible to keep all vectors in RAM, disk I\u002FO can become the bottleneck for throughput. \nIn this case:\n\n- Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in [Disk performance article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fmemory-consumption\u002F)\n- Use `io_uring` on Linux (kernel 5.11+) [io_uring article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fio_uring\u002F)\n- In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the [tutorial](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-operations\u002Flarge-scale-search\u002F?s=search-query)\n- Configure higher number of search threads to parallelize disk reads. Default is `cpu_count - 1`, which is optimal for RAM-based search but may be too low for disk-based search. See [configuration reference](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fconfiguration\u002F?s=configuration-options)\n- If still saturated, scale out horizontally (each node adds independent IOPS)\n\n\n## What NOT to Do\n\n- Do not expect to optimize throughput and latency simultaneously on the same node\n- Do not use many small segments for throughput workloads (increases per-query overhead)\n- Do not scale horizontally when IOPS-bound without also upgrading disk tier\n- Do not run at >90% RAM (OS cache eviction = severe performance degradation)\n","# Scaling for Query Throughput (QPS)\n\nThroughput scaling means handling more parallel queries per second. \nThis is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node.\n\nHigh throughput favors fewer, larger segments so each query touches less overhead.\n\n\n## Performance Tuning for Higher RPS\n\n- Use fewer, larger segments (`default_segment_number: 2`) [Maximizing throughput](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Foptimize\u002F?s=maximizing-throughput)\n- Enable quantization with `always_ram=true` to reduce disk IO [Quantization](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F)\n- Use batch search API to amortize overhead [Batch search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch\u002F?s=batch-search-api)\n\n## Minimize impact of Update Workloads\n\n- Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads [Low latency search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F)\n- Set `optimizer_cpu_budget` to limit indexing CPUs (e.g. `2` on an 8-CPU node reserves 6 for queries)\n- Configure delayed read fan-out (v1.17+) for tail latency [Delayed fan-outs](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F?s=use-delayed-fan-outs)\n\n\n\n## Horizontal Scaling for Throughput\n\nIf a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.\n\n- Shard replicas serve queries from replicated shards, distributing read load across nodes\n- Each replica adds independent query capacity without re-sharding\n- Use `replication_factor: 2+` and route reads to replicas [Distributed deployment](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fdistributed_deployment\u002F?s=replication)\n\nSee also [Horizontal Scaling](..\u002Fscaling-data-volume\u002Fhorizontal-scaling\u002FSKILL.md) for general horizontal scaling guidance.\n\n\n## Disk I\u002FO Bottlenecks\n\nIf it is not possible to keep all vectors in RAM, disk I\u002FO can become the bottleneck for throughput. \nIn this case:\n\n- Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in [Disk performance article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fmemory-consumption\u002F)\n- Use `io_uring` on Linux (kernel 5.11+) [io_uring article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fio_uring\u002F)\n- In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the [tutorial](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-operations\u002Flarge-scale-search\u002F?s=search-query)\n- Configure higher number of search threads to parallelize disk reads. Default is `cpu_count - 1`, which is optimal for RAM-based search but may be too low for disk-based search. See [configuration reference](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fconfiguration\u002F?s=configuration-options)\n- If still saturated, scale out horizontally (each node adds independent IOPS)\n\n\n## What NOT to Do\n\n- Do not expect to optimize throughput and latency simultaneously on the same node\n- Do not use many small segments for throughput workloads (increases per-query overhead)\n- Do not scale horizontally when IOPS-bound without also upgrading disk tier\n- Do not run at >90% RAM (OS cache eviction = severe performance degradation)\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fqdrant-scaling\u002Fscaling-qps",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":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add github\u002Fawesome-copilot --skill qdrant-scaling-qps",{"tool":57,"command":58},"bunx","bunx skills add github\u002Fawesome-copilot --skill qdrant-scaling-qps",{"tool":60,"command":61},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill qdrant-scaling-qps","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fqdrant-scaling\u002Fscaling-qps\u002FSKILL.md",true,[65],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"LICENSE.txt","LICENSE","许可证",11345,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Facademy-guide\u002FLICENSE.txt",[72,104,132,162,195,212],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":86,"publishedAt":36},"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",[78,83],{"categoryNo":79,"categoryName":80,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":84,"categoryName":85,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000023","Skill与智能体开发",[87,88,89,90,91,24,92,93,33,94,95,96,97,98,99,100,32,101,102,103],"supabase","rls","supabase rls","supabase rls policies","project","review","change","write","security","isolation","tables","as","tests","prove","users","functions","with",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":111,"publishedAt":36},"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",[110],{"categoryNo":84,"categoryName":85,"taxonomyType":81,"taxonomyTypeDesc":82},[112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":136,"sourceHtmlUrl":137,"categories":138,"keywords":144,"publishedAt":36},"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",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[145,146,147,148,149,150,118,91,151,152,153,154,155,156,157,32,158,159,160,161],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","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":36},"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":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},[174,31,175,176,177,178,179,180,181,182,183,184,185,186,187,24,188,189,190,191,192,118,193,194],"winmd api search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":200,"sourceHtmlUrl":201,"categories":202,"keywords":208,"publishedAt":36},"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",[203,204,207],{"categoryNo":170,"categoryName":171,"taxonomyType":81,"taxonomyTypeDesc":82},{"categoryNo":205,"categoryName":206,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":84,"categoryName":85,"taxonomyType":81,"taxonomyTypeDesc":82},[209,115,182,210,211],"resemble detect","https","identity",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":216,"sourceHtmlUrl":217,"categories":218,"keywords":223,"publishedAt":36},"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",[219,222],{"categoryNo":220,"categoryName":221,"taxonomyType":81,"taxonomyTypeDesc":82},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":84,"categoryName":85,"taxonomyType":81,"taxonomyTypeDesc":82},[224,225,226,227,146,92,9,114,228,229,210,230,231,232,233,234,235,236,237,238,239,240,99,241,242,243,244],"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"]