@qdrant

qdrant-search-quality diagnoses embedding model, configuration, and query strategy issues. guides root cause isolation via exact search testing, baseline measurement, parameter tuning, and hybrid/reranking approaches.

qdrant-performance-optimization is a navigation hub for vector search tuning that fragments its guidance across multiple skills, covering latency, throughput, and indexing considerations without actionable procedures.

qdrant-scaling categorizes scaling problems into data volume, throughput, latency, and query volume, then defers to external docs without providing actionable guidance, decision logic, or concrete procedures.

qdrant-clients-sdk lists supported client libraries across six languages with installation commands and api reference pointers, but omits integration workflows or concrete usage patterns.

Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which…

Guidance on how to upgrade your Qdrant version without interrupting the availability of your application and ensuring data integrity.

qdrant-model-migration — an installable skill for AI agents, published by qdrant/skills.

qdrant-monitoring — an installable skill for AI agents, published by qdrant/skills.