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Enterprise search: relevance tuning, query understanding, index management, search quality, ranking optimization, schema design.
Passed 20 of 22 test cases with the skill, and 17 of 22 without it.
and 1 more in the full report
3 fixed18 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have an OpenSearch 2.11 index for long-form technical articles where short titles receive excessive BM25 term frequency saturation compared to deep body text. We want to adjust term frequency saturation and document length normalization. How should we proceed?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies BM25 k1 and b parameters as the target settings for adjustment | Passed | Passed |
| Specifies capturing baseline evaluation metrics before modifying index or field parameters | Missed | Passed |
The instructions your model reads when the skill loads.
Search infrastructure design, relevance tuning, query understanding, index management, quality measurement, and performance optimization. Each mode loads its own reference files on demand.
Classify into one mode before proceeding.
| Mode | Signal Phrases | Reference |
|---|---|---|
| RELEVANCE | tune relevance, BM25, boost, function score, field weight, LTR, learned ranking, ranking model | references/relevance-tuning.md |
| QUERY | query understanding, intent classification, entity extraction, query expansion, synonyms, spell correction, query rewriting | references/query-understanding.md |
| INDEX | schema design, mapping, analyzer chain, reindex, alias, ILM, index template, field type | references/index-management.md |
| QUALITY | nDCG, MRR, precision, recall, search quality, judgment, A/B test, evaluation, search funnel | references/search-quality.md |
| PERFORMANCE | slow query, shard, cache, circuit breaker, scroll, search_after, query optimization, latency | references/performance-optimization.md |
| ARCHITECTURE | search architecture, hybrid search, vector search, pipeline design, platform selection, migration | (cross-reference: load relevant references based on sub-topic) |
If the request spans modes, pick the primary and note the secondary. ARCHITECTURE mode loads references from whichever sub-topics apply.
Load: references/relevance-tuning.md, references/llm-search-failure-modes.md
| Problem Class | Symptoms | Starting Point |
|---|---|---|
| Poor precision | Good results buried under noise | Field boosting, minimum_should_match |
| Poor recall | Known-good results missing | Analyzer tuning, query expansion, synonym filters |
| Wrong ordering | Right results, wrong rank | BM25 parameter tuning, function scoring |
| Domain mismatch | Generic scoring fails domain | Learned ranking (LTR), custom similarity |
| Freshness | Stale results ranked too high | Decay functions, recency boosts |
test cases passed, vs 17 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.