How do I install the Opensearch Query Review skill?

Run npx skillmds@latest add ch4570/opensearch-query-review in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Opensearch Query Review free to use?

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Who published Opensearch Query Review?

ch4570 (@ch4570) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Opensearch Query Review by ch4570 — Agent Skill

Name: Opensearch Query Review
Author: ch4570

Opensearch Query Review

Review OpenSearch 3.5 search queries. Inspect dynamic bool queries (filter vs query context, builder safety, paging), vector k-NN queries (k/ef_search, efficient filtering, under-fetch, radial, nested), hybrid (normalization/RRF)·neural/sparse/semantic, and search pipelines. Use when authoring/modifying queries or diagnosing search-result/latency problems. Ground claims in the OpenSearch official-docs KB.

ch4570 451f97f 8 files · 24.5 KB Updated Sep 17, 2026

File contents

OpenSearch Query Building Review (Query Review)

Grounding principles are in reference/principles.md. For facts, prefer reference/kb/ (official docs).

Review order (proceed exactly like this)

What the query is asking — result set, filter selectivity, frequency, sort/paging pattern.
Dynamic bool check — reference/kb/query-dsl-basics.md (filter context, term/match, builder safety).
Relevance·paging — reference/kb/relevance-scoring.md (BM25/boost/min_score) · reference/kb/pagination-search-after.md.
Vector/hybrid check — reference/kb/vector-hybrid-query.md (k vs ef_search, efficient filtering, normalization/RRF). If hybrid/neural grep returns 0, state "not applicable".
Slowness diagnosis — reference/kb/profiling-explain.md (Profile API). Provide a fix (query diff) with severity + re-measurement (recall@k·p99).

Quick checklist

Is a no-score condition in must? → filter/must_not (skip scoring + cache).
Does the dynamic builder enforce a field whitelist, a terms size cap, and avoid script/wildcard?
Is deep paging using from+size? → search_after/PIT.
In k-NN, is ef_search ≥ k? A variable k with a fixed ef_search means a ceiling mismatch.
When a filter is present, is it efficient k-NN filtering, and does a selective filter under-fetch (over-fetch/exact fallback)?
(hybrid) Is there a normalization/RRF processor? Are the neural ingest↔query model_id the same?
Did a type-based client work around terms via bool.should? → replace with the native form.

KB (read and cite first)

From reference/kb/INDEX.md: reference/kb/query-dsl-basics.md · reference/kb/relevance-scoring.md · reference/kb/vector-hybrid-query.md · reference/kb/pagination-search-after.md · reference/kb/profiling-explain.md

Deliverable

For each finding: problem → principle/KB source → query diff → re-measurement (recall@k sweep·p99). "It got faster" is confirmed only by measurement.

---name: opensearch-query-reviewdescription: Review OpenSearch 3.5 search queries. Inspect dynamic bool queries (filter vs query context, builder safety, paging), vector k-NN queries (k/ef_search, efficient filtering, under-fetch, radial, nested), hybrid (normalization/RRF)·neural/sparse/semantic, and search pipelines. Use when authoring/modifying queries or diagnosing search-result/latency problems. Ground claims in the OpenSearch official-docs KB.---# OpenSearch Query Building Review (Query Review)Grounding principles are in `reference/principles.md`. For facts, prefer `reference/kb/` (official docs).## Review order (proceed exactly like this)1. **What the query is asking** — result set, filter selectivity, frequency, sort/paging pattern.2. **Dynamic bool** check — `reference/kb/query-dsl-basics.md` (filter context, term/match, builder safety).3. **Relevance·paging** — `reference/kb/relevance-scoring.md` (BM25/boost/min_score) · `reference/kb/pagination-search-after.md`.4. **Vector/hybrid** check — `reference/kb/vector-hybrid-query.md` (k vs ef_search, efficient filtering, normalization/RRF). **If hybrid/neural grep returns 0, state "not applicable"**.5. **Slowness diagnosis** — `reference/kb/profiling-explain.md` (Profile API). Provide a fix (query diff) with severity + re-measurement (recall@k·p99).## Quick checklist- [ ] Is a no-score condition in `must`? → `filter`/`must_not` (skip scoring + cache).- [ ] Does the dynamic builder enforce a field whitelist, a `terms` size cap, and avoid `script`/wildcard?- [ ] Is deep paging using `from`+`size`? → `search_after`/PIT.- [ ] In k-NN, is `ef_search ≥ k`? A variable `k` with a fixed `ef_search` means a ceiling mismatch.- [ ] When a filter is present, is it efficient k-NN filtering, and does a selective filter under-fetch (over-fetch/exact fallback)?- [ ] (hybrid) Is there a normalization/RRF processor? Are the neural ingest↔query model_id the same?- [ ] Did a type-based client work around `terms` via `bool.should`? → replace with the native form.## KB (read and cite first)From `reference/kb/INDEX.md`: `reference/kb/query-dsl-basics.md` · `reference/kb/relevance-scoring.md` · `reference/kb/vector-hybrid-query.md` · `reference/kb/pagination-search-after.md` · `reference/kb/profiling-explain.md`## DeliverableFor each finding: problem → principle/KB source → query diff → re-measurement (recall@k sweep·p99). "It got faster" is confirmed only by measurement.

Frequently asked questions