qdrant/skills
πͺMarketplaceqdrant/skills
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, Java
Add this Marketplace
/plugin marketplace add qdrant/skillsPlugins in this Marketplace
More from this repository8
Reference skill for the officially supported Qdrant client SDKs β Python, JavaScript/TypeScript, Rust, Go, .NET, and Java β plus REST and gRPC API guidance for integrating with a Qdrant deployment.
Diagnose and improve Qdrant search relevance β isolate whether the issue is the embedding model, chunking, HNSW tuning, quantization, or query strategy, with sub-skills for diagnosis/tuning and hybrid/reranking search strategies.
Guides coding agents through Qdrant performance tuning β search speed, memory usage, and indexing throughput β with concrete decisions around quantization, HNSW parameters, and payload indexing.
Official Qdrant skill for **monitoring & observability** β splits into Monitoring Setup (Prometheus scraping, health probes, Hybrid Cloud specifics, alerting, log centralization) and Debugging with Metrics (optimizer stuck, memory growth, slow requests); branches based on whether the user is setting up monitoring or diagnosing an active production issue.
Official Qdrant skill for **scaling decisions** β first nails down the goal (data volume / query throughput (QPS) / query latency / query volume), then routes to the right playbook via sub-skills (`scaling-data-volume`, `scaling-qps`, `minimize-latency`, `scaling-query-volume`) since throughput and latency tuning pull in opposite directions.
Official Qdrant skill for **deployment options** β guides coding agents through choosing between local development, self-hosted (Docker/Kubernetes), Qdrant Cloud, and hybrid deployment modes, with configuration and trade-off analysis for each approach.
Official Qdrant skill for **embedding-model migration** without downtime β enforces "you must create a new collection, named vectors can't be added later", offers zero-downtime alias swap, side-by-side multi-vector A/B, Matryoshka-dimensions shortcut, denseβhybrid (BM25) path, and bulk-re-embed tips (`update_mode: insert`, disabled HNSW, parallel batches, ColBERT co-location warning).
Official Qdrant skill covering **safe version upgrades** β compatibility guarantees, rolling upgrade procedures, and upgrade path validation so coding agents can confidently navigate Qdrant version transitions without downtime or data loss.