Qdrant Search Strategies

Qdrant Search Strategies is an AI Engineering skill for Claude Code, published by qdrant in skills.

230 stars28 forkson qdrant/skillsAdded 2026/09/03Repository updated 2026/09/02

agent-skillsai-agentsclaude-codecodexcursorembeddingshybrid-searchmonitoringmultitenancyperformanceqdrantquantizationscalingsearch-qualityvector-databasevector-search

Install in seconds

Install Qdrant Search Strategies

Copy Qdrant Search Strategies into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/qdrant/skills/tree/main/skills/qdrant-search-quality/search-strategies ~/.claude/skills/search-strategies

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/qdrant/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/qdrant-search-quality/search-strategies/SKILL.md in qdrant/skills
Installs to: ~/.claude/skills/search-strategies
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2793 skills

What Qdrant Search Strategies does

Qdrant Search Strategies helps choose advanced retrieval methods when basic vector search misses relevant items or ranks them poorly. Use it for hybrid search, reranking, relevance feedback, MMR, recommendations, discovery, and score boosting.

Qdrant Search Strategies is cataloged under AI Engineering on DirSkills. Qdrant Search Strategies comes from a repository tagged agent-skills, ai-agents, claude-code, codex and cursor.

Documentation

README

Excerpt

How to Improve Search Results with Advanced Strategies

These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first. If the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality.

Missing Keyword Matches or Need to Combine Multiple Search Signals

Use when: pure vector search misses keyword/domain term matches, or the use case benefits from combining searches on multiple representations (including languages and modalities) of the same item.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Qdrant Search Strategies

What else does qdrant publish alongside Qdrant Search Strategies?
Qdrant Search Strategies is one of 25 skills that DirSkills catalogs from qdrant/skills, the repository it ships in. Its siblings there include Qdrant Advisor, Qdrant Clients SDK and Qdrant Deployment Options. Each one is a separate skill with its own page in this directory, installs the same way Qdrant Search Strategies does, and is maintained by qdrant in that same repository. The rest of the collection is listed on the qdrant/skills page.
How does Qdrant Search Strategies compare to other AI Engineering skills?
Qdrant Search Strategies ranks #2484 by stars among the 2793 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Qdrant Search Strategies against them. Open each page to compare what they document and how they install.