Qdrant Search Quality

From the author

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.

— the description by anush008

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Qdrant Search Quality provides a structured approach to troubleshooting and optimizing search performance within Qdrant. It helps users isolate the root causes of poor search results, whether they stem from embedding model selection, data chunking strategies, or Qdrant-specific configuration settings. The skill guides users through diagnostic steps to determine if issues arise from the underlying data representation or the search parameters, offering pathways to improve precision and recall through techniques like hybrid search and reranking.

What it does

Diagnoses causes of low precision, low recall, or irrelevant search matches.
Evaluates the impact of embedding models on search relevance.
Provides guidance on optimizing data chunking to prevent quality loss.
Assists in tuning HNSW parameters for better search performance.
Recommends advanced strategies like hybrid search and reranking to improve results.

When to use it

When users report that search results are inaccurate or irrelevant.
When search quality drops after changing embedding models or applying quantization.
When troubleshooting performance degradation following significant data growth.
When deciding between different search strategies like hybrid search or reranking.

Advantages

Systematic approach to isolating search quality issues.
Focuses on high-impact factors like data chunking and model selection.
Provides clear paths for both configuration tuning and architectural improvements.

Usage examples

“My search results in Qdrant are irrelevant, how can I improve them?”: The agent will guide you through a diagnostic process to check your embedding model, verify your data chunking strategy, and evaluate if hybrid search or reranking could improve your results.
“Should I use reranking to fix my low precision issues?”: The agent will explain how reranking functions within the Qdrant ecosystem and help you determine if it is the appropriate solution for your specific precision problems.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/github/qdrant-search-quality/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-search-quality.zip -d .agents/skills

Documentation by anush008

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

Files

ATTRIBUTION.mdAdded by AutoGineer875 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md1.3 KiB

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Anndata
K-Dense· Community
A Python framework for managing annotated data matrices in single-cell genomics and general research workflows.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskGitHub stars of the source repository56.4k
Liteparse
K-Dense· Community
Local document and PDF parsing with spatial text extraction and bounding box support.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
Apache-2.0Low riskHas scriptsGitHub stars of the source repository56.4k
Usfiscaldata
K-Dense· Community
Access U.S. Treasury federal financial data via the Fiscal Data REST API.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskGitHub stars of the source repository56.4k
Geopandas
K-Dense· Community
Guidance and local audit tools for Python workflows using GeoPandas 1.2.0 for spatial data analysis.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskHas scriptsGitHub stars of the source repository56.4k
Markitdown
K-Dense· Community
A lightweight utility for converting diverse document formats into structured Markdown for analysis, search, and LLM ingestion.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskHas scriptsGitHub stars of the source repository56.4k
Zarr Python
K-Dense· Community
A guide for storing and querying chunked N-D scientific arrays using Zarr-Python 3.
Data and AIClaude CodeClaude appsOpenAI Codex+14 more
MITLow riskGitHub stars of the source repository56.4k