qdrant-search-quality-diagnosis
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.
Profile →npx skills add github/awesome-copilot --skill diagnosis --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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](https://search.qdrant.tech/md/documentation/tutorials-search-engineering/retrieval-quality/?s=standard-mode-vs-exact-search) - 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](https://search.qdrant.tech/md/documentation/search/search/?s=grouping-api) 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. - Inc
- Don't Know What's Wrong Yet
- Approximate Search Worse Than Exact
- Wrong Embedding Model
- Unoptimized Search Pipeline
- What NOT to Do
What does the qdrant-search-quality-diagnosis skill do?
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 do I install it?
Run `npx skills add github/awesome-copilot --skill diagnosis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.