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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

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qdrant
26733 skills

Claude Code

$ npx -y skills add qdrant/skills --skill qdrant-search-quality --agent claude-code

SKILL.md

qdrant-search-quality.SKILL.md
name: qdrant-search-quality
description: "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?', 'how to combine keyword and vector search / fusion / RRF / prefetch?', 'should I use reranking?', 'relevance feedback?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', 'how to score recall@k?', 'is my improvement real / statistically significant?', or 'should I fine-tune my embedding model?'. Also use when search quality degrades after quantization, model change, or data growth."
allowed-tools:
  - Read
  - Grep
  - Glob

Qdrant Search Quality

Route first, then answer. Match the user's symptom in the table, `Read` that file, and answer from it. Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.

| The user says | Read | |---|---| | Search results are bad or irrelevant, wrong results, missing expected matches | `diagnosis/SKILL.md` | | Low recall, expected results are missing | `diagnosis/SKILL.md` | | Low precision, too many wrong matches | `diagnosis/SKILL.md` | | Which embedding model to use, quality dropped after quantization, model change, or data growth | `diagnosis/SKILL.md` | | Not sure if the model, the data, or Qdrant is at fault | `diagnosis/SKILL.md` | | Want to measure recall, build a golden set, ground truth dataset, recall@k | `diagnosis/SKILL.md` | | Is my improvement real, statistically significant, fine-tune the embedding model | `diagnosis/SKILL.md` | | Need to combine keyword and semantic search, hybrid search, sparse + dense, fusion / RRF, prefetch | `search-strategies/hybrid-search/SKILL.md` | | Should I rerank, results too similar, need diversity, MMR, recommendation/discovery API | `search-strategies/SKILL.md` | | Improving results with relevance feedback or user clicks, cheaper alternative to reranking | `search-strategies/relevance-feedback/SKILL.md` |

Most quality issues come from the embedding model or the data, not from Qdrant's configuration — splitting chunks mid-sentence alone can drop quality 30-40%. Rule that out with exact search before tuning any Qdrant parameter: [Search API](https://skills.qdrant.tech/md/documentation/search/search/?s=search-api)

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@qdrant
Ships withqdrant

Agent skills for building with Qdrant vector search Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model

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Questions about qdrant-search-quality.

Which coding agents can use qdrant-search-quality?
qdrant-search-quality installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.