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Official agent skill

Qdrant Search Quality Diagnosis

by github in github/awesome-copilot

Diagnoses Qdrant search quality issues. An agent skill from github/awesome-copilot.

OfficialMITAuto-check passedAI & LLM Engineering

Install Qdrant Search Quality Diagnosis

skills CLI
$ npx skills add github/awesome-copilot --skill qdrant-search-quality-diagnosis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot qdrant-search-quality-diagnosis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/qdrant-search-quality/diagnosis .claude/skills/qdrant-search-quality-diagnosis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qdrant-search-quality-diagnosis
GitHub stars
40k
Used in
1 other repo
Token cost
~928 tokens
SKILL.md length
359 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Diagnoses Qdrant search quality issues. An agent skill from github/awesome-copilot.

  • Someone reports results are bad
  • SKILL.md covers Don't Know What's Wrong Yet, Approximate Search Worse Than…, Wrong Embedding Model and Unoptimized Search Pipeline, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Not relevant results

What it does

Qdrant Search Quality Diagnosis is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', or 'quality dropped after quantization'. Also use when search quality degrades without obvious changes.

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Vector databases, LLM inference and serving and Embeddings. It works with Qdrant. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Someone reports results are bad
  • Not relevant results
  • Missing matches
  • Approximate search worse than exact

Example prompts

  • “results are bad”
  • “wrong results”
  • “not relevant results”
  • “/qdrant-search-quality-diagnosis”

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • search.qdrant.tech

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qdrant Search Quality Diagnosis loads about 928 tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 359 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~928

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 359 words, ~928 tokens.

Download SKILL.mdSave it as .claude/skills/qdrant-search-quality-diagnosis/SKILL.md (or your agent's skills folder).
name
qdrant-search-quality-diagnosis
description
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', or 'quality dropped after quantization'. Also use when search quality degrades without obvious changes.

How to Diagnose Bad Search Quality

Before tuning, establish baselines. Use exact KNN as ground truth, compare against approximate HNSW. Target >95% recall@K for production.

Don't Know What's Wrong Yet

Use when: results are irrelevant or missing expected matches and you need to isolate the cause.

  • Test with exact=true to bypass HNSW approximation Search API
  • Exact search bad = model or search pipeline problem. Exact good, approximate bad = tune HNSW.
  • Check if quantization degrades quality (compare with and without)
  • Check if filters are too restrictive (then you might need to use ACORN)
  • If duplicate results from chunked documents, use Grouping API to deduplicate Grouping

Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering. Text content goes in sparse vectors for search.

Approximate Search Worse Than Exact

Use when: exact search returns good results but HNSW approximation misses them.

Binary quantization requires rescore. Without it, quality loss is severe. Use oversampling (3-5x minimum for binary) to recover recall. Always test quantization impact on your data before production. Quantization

Show full SKILL.md (145 more words)Show less

Wrong Embedding Model

Use when: exact search also returns bad results.

Test top 3 MTEB models on 100-1000 sample queries, measure recall@10. Domain-specific models often outperform general models. Hosted inference

Unoptimized Search Pipeline

Use when: exact search also returns bad results and model choice is confirmed by user.

Optimize search according to advanced search-strategies skill.

What NOT to Do

  • Tune Qdrant before verifying the model is right for the task (most quality issues are model issues)
  • Use binary quantization without rescore (severe quality loss)
  • Set hnsw_ef lower than results requested (guaranteed bad recall)
  • Skip payload indexes on filtered fields then blame quality (HNSW can't traverse filtered-out nodes, and filterable HNSW is built only if payload indexes were set up prior)
  • Deploy without baseline recall or other search relevance metrics (no way to measure regressions)
  • Confuse payload filtering with sparse vector search (different things, different config)

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/qdrant-search-quality/diagnosis of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Qdrant Search Quality Diagnosis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qdrant Search Quality Diagnosis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qdrant Search Quality Diagnosis this skillgithub/awesome-copilot40k1 repos~928Automated safety check: PassMIT
Qdrant Search Quality Diagnosisqdrant/skills253—~2.3kAutomated safety check: PassApache-2.0
Vector DBericrisco/rsc-harness156—~2.8kAutomated safety check: PassMIT
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0
RAG Implementationwshobson/agents40k9 repos~1.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Qdrant Search Quality Diagnosis

What does Qdrant Search Quality Diagnosis do?

Diagnoses Qdrant search quality issues. An agent skill from github/awesome-copilot. Qdrant Search Quality Diagnosis is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Diagnoses Qdrant search quality issues.

When should I use Qdrant Search Quality Diagnosis?

Qdrant Search Quality Diagnosis fits situations like: someone reports results are bad; not relevant results; missing matches; approximate search worse than exact.

How do I install Qdrant Search Quality Diagnosis in Claude Code?

Run `npx skills add github/awesome-copilot --skill qdrant-search-quality-diagnosis -a claude-code`. Or copy the skill folder (skills/qdrant-search-quality/diagnosis in github/awesome-copilot) into .claude/skills/qdrant-search-quality-diagnosis in your project. Claude Code loads it when a task matches its description.

How do I install Qdrant Search Quality Diagnosis in Codex?

Run `npx skills add github/awesome-copilot --skill qdrant-search-quality-diagnosis -a codex`. Or copy the skill folder (skills/qdrant-search-quality/diagnosis in github/awesome-copilot) into .agents/skills/qdrant-search-quality-diagnosis in your project. Codex loads it when a task matches its description.

Can I use Qdrant Search Quality Diagnosis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill qdrant-search-quality-diagnosis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qdrant-search-quality-diagnosis, .gemini/skills/qdrant-search-quality-diagnosis, .github/skills/qdrant-search-quality-diagnosis and .opencode/skills/qdrant-search-quality-diagnosis in your project.

What does Qdrant Search Quality Diagnosis need to run?

SKILL.md names no scripts, command-line tools or credentials: Qdrant Search Quality Diagnosis is instructions for the agent only.

Does Qdrant Search Quality Diagnosis access the network?

SKILL.md names 1 domain. As links in the text: search.qdrant.tech. This is read from the text; nothing was executed.

Is Qdrant Search Quality Diagnosis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qdrant Search Quality Diagnosis use?

Qdrant Search Quality Diagnosis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qdrant Search Quality Diagnosis use?

About 928 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qdrant Search Quality Diagnosis?

Skills that share tags, products or a category with Qdrant Search Quality Diagnosis: Qdrant Search Quality Diagnosis (qdrant/skills, 253 stars), Vector DB (ericrisco/rsc-harness, 156 stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars) and Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qdrant Search Quality Diagnosis?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.