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Diagnoses and improves Qdrant search relevance.
Passed 7 of 16 test cases with the skill, and 2 of 16 without it.
and 5 more in the full report
7 fixed3 passed either way12 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/11/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to use vector context points and negative constraints via Qdrant's discovery API to refine product search results. Which strategy guide details this?
| Checked | Without skill | With skill |
|---|---|---|
| Directs the user to search-strategies/SKILL.md for discovery API usage | Missed | Passed |
The instructions your model reads when the skill loads.
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
test cases passed, vs 2 / 16 without
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