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Run npx skillmds@latest add ch4570/opensearch-optimization 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 Optimization free to use?

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Who published Opensearch Optimization?

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

Opensearch Optimization by ch4570 — Agent Skill

Name: Opensearch Optimization
Author: ch4570

Opensearch Optimization

Tune OpenSearch 3.5 vector/search optimization parameters and diagnose operational risk. Covers HNSW/IVF parameters (m/ef_construction/ef_search/nlist/nprobes), quantization (SQ/PQ/BQ)·on_disk, memory estimation· off-heap·circuit breaker, indexing tuning (refresh/replica/force merge)·shard sizing·ISM·caching. Use for performance/memory/recall tuning and migration/operational-safety checks. Grounded in the official-docs KB.

ch4570 4860c77 8 files · 22.5 KB Updated Sep 17, 2026

File contents

OpenSearch Optimization / Operational Risk (Optimization)

Grounding principles are in reference/principles.md. For facts, prefer reference/kb/.

Review order (proceed in this order)

Confirm scale — N·QPS·p99 SLO·RAM/heap·dimension. If absent, ask or state assumptions explicitly.
Vector parameters/memory — reference/kb/vector-tuning-memory.md (HNSW/IVF, ef_search≥k, quantization·off-heap).
Indexing — reference/kb/indexing-throughput.md (bulk·refresh/replica toggle·restore path) + reference/kb/refresh-merge-segments.md (force merge).
Cluster — reference/kb/shard-sizing.md (shards·ISM) + reference/kb/query-caching.md (cache·breaker).
Operational anti-patterns — triple cross-check of default/yml/test, temporary settings lingering in production, tests pinned to a defective value, mismatched variable–fixed parameter pairs. Severity + recommended parameters (current→recommended) + verification (recall@k·p99·_knn/stats).

Quick checklist

Is ef_search ≥ maximum allowed k (verify on boot)? Secure recall headroom (1.5~2× k).
Memory savings SQ (fp16) first. Measured N → estimation formula → numerically compute the circuit breaker (50%) headroom.
Are you trying to solve k-NN OOM by increasing heap (use off-heap instead)?
For bulk loads with replicas:0/refresh:-1, is there a restore code path? force merge only after writes finish.
For time-series indices, is there ISM·_vN+alias swap?
Do "temporary/diagnostic" timeouts·caps linger in production? Is there a test that pins a defective value as the expected value?

KB (read and cite first)

From reference/kb/INDEX.md: reference/kb/indexing-throughput.md · reference/kb/refresh-merge-segments.md · reference/kb/shard-sizing.md · reference/kb/query-caching.md · reference/kb/vector-tuning-memory.md

Deliverable

For each finding: problem → principle/KB source → recommended parameters (current→recommended, with rationale) → verification command (_knn/stats·_cat/indices·recall@k sweep). Measure→hypothesize→adjust→re-measure. Reject any tuning that does not look at recall@k and p99 together.

---name: opensearch-optimizationdescription: Tune OpenSearch 3.5 vector/search optimization parameters and diagnose operational risk. Covers HNSW/IVF parameters (m/ef_construction/ef_search/nlist/nprobes), quantization (SQ/PQ/BQ)·on_disk, memory estimation· off-heap·circuit breaker, indexing tuning (refresh/replica/force merge)·shard sizing·ISM·caching. Use for performance/memory/recall tuning and migration/operational-safety checks. Grounded in the official-docs KB.---# OpenSearch Optimization / Operational Risk (Optimization)Grounding principles are in `reference/principles.md`. For facts, prefer `reference/kb/`.## Review order (proceed in this order)1. **Confirm scale** — N·QPS·p99 SLO·RAM/heap·dimension. If absent, ask or state assumptions explicitly.2. **Vector parameters/memory** — `reference/kb/vector-tuning-memory.md` (HNSW/IVF, ef_search≥k, quantization·off-heap).3. **Indexing** — `reference/kb/indexing-throughput.md` (bulk·refresh/replica toggle·restore path) + `reference/kb/refresh-merge-segments.md` (force merge).4. **Cluster** — `reference/kb/shard-sizing.md` (shards·ISM) + `reference/kb/query-caching.md` (cache·breaker).5. **Operational anti-patterns** — triple cross-check of default/yml/test, temporary settings lingering in production, tests pinned to a defective value, mismatched variable–fixed parameter pairs. Severity + recommended parameters (current→recommended) + verification (recall@k·p99·`_knn/stats`).## Quick checklist- [ ] Is `ef_search ≥ maximum allowed k` (verify on boot)? Secure recall headroom (1.5~2× k).- [ ] Memory savings SQ (fp16) first. Measured N → estimation formula → **numerically compute** the circuit breaker (50%) headroom.- [ ] Are you trying to solve k-NN OOM by increasing heap (use off-heap instead)?- [ ] For bulk loads with `replicas:0`/`refresh:-1`, is there a **restore code path**? force merge only after writes finish.- [ ] For time-series indices, is there ISM·`_vN`+alias swap?- [ ] Do "temporary/diagnostic" timeouts·caps linger in production? Is there a test that pins a defective value as the expected value?## KB (read and cite first)From `reference/kb/INDEX.md`: `reference/kb/indexing-throughput.md` · `reference/kb/refresh-merge-segments.md` · `reference/kb/shard-sizing.md` · `reference/kb/query-caching.md` · `reference/kb/vector-tuning-memory.md`## DeliverableFor each finding: problem → principle/KB source → recommended parameters (current→recommended, with rationale) → verification command (`_knn/stats`·`_cat/indices`·recall@k sweep). Measure→hypothesize→adjust→re-measure. Reject any tuning that does not look at recall@k and p99 together.

Frequently asked questions