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From qdrant
Diagnoses Qdrant search relevance issues (poor results, low precision/recall) and guides tuning of embedding models, HNSW parameters, query strategies, and hybrid search with reranking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/qdrant:qdrant-search-qualityThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.
First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.
Isolate the source of quality issues, establish labeled baselines to measure recall and relevance, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning
Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies
npx claudepluginhub qdrant/skills --plugin qdrantOptimizes Qdrant vector search performance covering indexing strategies, query optimization, search speed, indexing performance, and memory usage. Use to improve speed and efficiency of Qdrant deployment.
Optimizes vector index performance by tuning HNSW parameters, selecting quantization strategies, and balancing latency, recall, and memory for production-scale vector search.
Tunes vector indexes for latency, recall, and memory using HNSW parameters, quantization strategies, and scaling guidelines up to billions of vectors.