Agent skill · data analytics · github
qdrant-search-quality
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth.
Why this skill is useful
Provides specific strategies and tuning parameters for improving Qdrant search relevance that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
Qdrant Search Quality 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%. Start by testing with exact search to isolate the problem Search API Diagnosis and Tuning Isolate the source of quality issues, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning Search Strategies Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/qdrant-search-quality