Amazon Opensearch Service

AWSOfficialPublished by AWS

Skill
Workflow
Cloud and DevOps
Apache-2.0
Low risk

From the author

Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.

— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a unified framework for interacting with Amazon OpenSearch Service (AOS) and OpenSearch Serverless (AOSS). It covers the full lifecycle of OpenSearch operations, including migration from legacy systems like Solr or Elasticsearch, infrastructure provisioning, advanced search implementation (including vector and RAG), and observability through log and trace analytics. It also features an Agentic AI Assistant for natural language data exploration and incident investigation, ensuring users can navigate complex cluster management and data analysis tasks without manual query crafting.

What it does

Guides migration workflows from Solr, Elasticsearch, or self-managed OpenSearch to AOS/AOSS.
Assists with provisioning, lifecycle management, and monitoring of domains and collections.
Facilitates implementation of vector, semantic, and hybrid search, including RAG with Bedrock.
Supports log and trace analytics using PPL, OpenTelemetry, and anomaly detection.
Provides an Agentic AI Assistant for natural language querying and automated incident root cause analysis.
Enforces operational best practices for sharding, storage tiering, and security configurations.

When to use it

When planning a migration from legacy search engines to Amazon OpenSearch Service.
When setting up or optimizing search capabilities, including vector search and RAG.
When troubleshooting cluster health, performance, or shard allocation issues.
When performing log or trace analysis to investigate system incidents.
When you need to generate optimized PPL or DSL queries using natural language.

Advantages

Centralizes complex operational knowledge across six distinct OpenSearch capabilities.
Reduces manual query complexity by leveraging the Agentic AI Assistant.
Provides clear, actionable guidance on production failure modes and migration blockers.
Standardizes interactions through consistent CLI and data-plane patterns.
Promotes architectural best practices for scaling and cost-effective storage tiering.

Usage examples

“How do I migrate my existing Solr index to Amazon OpenSearch Service?”: The agent identifies the migration capability, explains that Solr to OpenSearch is a refactor migration, and guides you through using the Migration Assistant for data backfill.
“Why is my cluster status yellow and how can I fix it?”: The agent uses the AI Assistant capability to analyze cluster health, check shard allocation, and provide specific commands or steps to resolve unassigned shards.
“Help me set up a RAG pipeline using Bedrock and my OpenSearch domain.”: The agent routes to the search capability, providing instructions on configuring Bedrock connectors and vector index mappings for semantic retrieval.
“Find the root cause of the 503 errors in my application logs from the last hour.”: The agent utilizes the AI Assistant to query logs, perform time-series analysis, and correlate findings to identify the likely root cause of the errors.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/amazon-opensearch-service/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip amazon-opensearch-service.zip -d .agents/skills

Documentation by AWS

Amazon OpenSearch Service — the unified skill

This skill answers anything about Amazon OpenSearch Service or Serverless across six capabilities. Step 0 below routes the question to ONE capability and points at that capability's entry-point reference. Everything else — when to dispatch, sub-references, capability-specific facts, cross-capability links — lives in the entry-point reference for that capability.

AWS MCP server is recommended, not required. Capability references show standard AWS CLI commands as the primary syntax (e.g., aws opensearch describe-domain, aws opensearchserverless create-collection). Where the AWS MCP server is available, its call_aws tool offers a streamlined alternative — but every operation in this skill MUST work via the AWS CLI alone. Data-plane HTTP calls against AOS / AOSS use awscurl for SigV4-signed requests; this works in both contexts.

Step 0: detect the capability — first thing you do

Pick one of the six capabilities below. State the detected capability in your first sentence (e.g., "Detected capability: SEARCH — semantic search setup with Bedrock embeddings."). Then load the entry-point reference; that file describes when to dispatch, indexes the rest of the capability's files, and routes you to the next step.

Capability	Entry-point reference
migration — Solr / Elasticsearch / self-managed OpenSearch into AOS or AOSS. Schema/query translation, sizing, cutover.	`references/assessment-workflow.md`
provisioning — Provisioning and managing AOS domains and AOSS collections. Lifecycle, upgrades, storage tiers, FGAC, monitoring.	`references/provisioning-reference.md`
search — Vector / semantic / hybrid / sparse / dense / RAG retrieval. Bedrock connectors, FAISS HNSW vs Lucene.	`references/search-semantic-search-guide.md`
log-analytics — Log search, observability, PPL, OSI ingestion, anomaly detection, OpenSearch Dashboards. Splunk/Datadog/ELK alternatives.	`references/log-analytics-guide.md`
trace-analytics — Distributed traces with OpenTelemetry. Span queries, service maps, Data Prepper.	`references/trace-analytics-trace-queries.md`
ai-assistant — Agentic AI Assistant: auto-discovers indices, generates optimized PPL/DSL queries, summarizes results, and investigates incidents end-to-end. No manual query crafting needed.	`references/ai-assistant.md`

If a prompt spans capabilities (e.g., "migrate from Solr AND set up RAG on the new domain"), pick the dominant capability for the response and close with a one-line handoff to the other capability's entry-point ref.

Universal rules (apply to ALL capabilities)

These rules apply to every response, regardless of capability. Capability-specific rules (sizing math, shape detection, Migration Assistant for Amazon OpenSearch Service capability matrix, k-NN engine selection) live in the entry-point references, not here.

Report header (every multi-section response). Begin every multi-section response with a single fenced metadata block: > Generated: <ISO 8601 timestamp> | Skill: amazon-opensearch-service v<N>. Get the time by calling the current_time tool (returns ISO 8601 in UTC). Read the skill version from this file's frontmatter version: field. For one-line answers (terse FOCUSED_OPERATIONAL replies, anti-pattern refusals) the header is optional; for any multi-section deliverable it is REQUIRED. Place it immediately after the report title and before the first ## heading.
No dollar estimates (HARD CONSTRAINT). Never produce $X/month, ~$1,500, or any dollar figure. Route every cost question to https://calculator.aws and stop. If a sub-reference contains dollar figures, treat them as informational context only and do NOT pass them through to the user.
No credential leakage (HARD CONSTRAINT). Never include master usernames, KMS key ARNs, VPC endpoint URLs, instance IPs, or account IDs in generated output.
Pick one for every A-vs-B decision. Name a primary recommendation in one line with a one-sentence reason. A "go with B if..." caveat is allowed AFTER the primary; never lead with conditional-only guidance.
Source restatement. The first 2–3 sentences must restate the source (engine + version + scale) when known, or restate the customer's question in concrete terms. The very first text the user sees must NOT be tool narration, meta-commentary, the report title, or simply restating the question verbatim.
No marketing tone. Do NOT use "seamless", "robust", "best-in-class", "production-hardened", "enterprise-grade", "world-class", "cleanly", "elegant". Do NOT stack 3+ vague hedges ("typically", "generally", "usually", "in most cases") in a single recommendation — be specific about when it does and does not apply.
Cross-capability handoff. When a user prompt spans capabilities (e.g., "migrate from Solr AND set up RAG on the new domain"), pick the dominant capability for the response, then close with a one-line handoff: "For <other capability>, see references/<other-capability>-<entry>.md."

Cross-cutting references (used across multiple capabilities)

These references are not capability-prefixed because they apply across capabilities. Capability entry-point references load them when relevant; SKILL.md never loads them directly.

references/sizing.md — sizing math, instance family details, OR1 trade-offs, watermarks, JVM heap rules.
references/vector-knn.md — k-NN engines, memory math, RAG ingestion patterns, ELSER alternatives.
references/observability.md — log analytics patterns, ISM, UltraWarm/Cold tiering, Splunk/Datadog migration playbooks.
references/security.md — FGAC, encryption, VPC patterns, audit logs, compliance posture.
references/personas.md — communication style per persona.
references/assessment-gotchas.md — production gotcha catalog (cite by number in Migration specifics or Risks/blockers tables; each gotcha carries a Category: tag that determines its lane).
references/assessment-knowledge-retrieval.md — topic → tool → URL recipe for batched verification.

Assets (assets/): report templates for FULL_ASSESSMENT renderings (Solr-source, ES-source, executive summary).

What this skill does NOT do

Estimate dollar costs. Pricing changes monthly and account-specific (RI, Savings Plan, EDP) discount math is outside this skill's reliable scope. Use https://calculator.aws.
Move data. Use Migration Assistant for Amazon OpenSearch Service (Historical Data Migration for backfill, Live Traffic Migration for live cutover).
Build embedding models. Use Amazon Bedrock or SageMaker.
Replace Splunk SPL or Datadog APM 1:1. Some queries / detectors / dashboards need rewriting.
Tune relevance for a specific catalog. Use OpenSearch Benchmark big5 workload + your own judgment list.

Guardrail — where this skill's own files live (MCP vs local install)

This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference or running a script:

Loaded through the AWS MCP server's retrieve_skill tool: The skill is not installed on the local filesystem. You MUST fetch each reference or script via retrieve_skill with the file parameter (e.g. file="references/architecture.md" or file="scripts/deploy.py"), and run the script from the returned content. Do NOT file_read these paths locally — they do not exist on disk.
Installed locally (e.g. .kiro/skills/your-skill/ or ~/.claude/skills/your-skill/): Read and run files from the local skill directory using relative paths.

This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory. Never fetch or write customer data through retrieve_skill.

Files

ATTRIBUTION.mdAdded by AutoGineer930 B
LICENSELicence file added by AutoGineer9.9 KiB
NOTICELicence file added by AutoGineer67 B
SKILL.md9.2 KiB
assets/elasticsearch-gap-register.md5.1 KiB
assets/elasticsearch-index-template-skeleton.md4.2 KiB
assets/elasticsearch-report-template.md7.8 KiB
assets/executive-summary-template.md3.0 KiB
assets/report-template.md12.9 KiB
assets/solr-gap-register.md3.8 KiB
assets/solr-index-template-skeleton.md3.7 KiB
assets/solr-report-template.md5.9 KiB
assets/tech-deepdive-template.md6.2 KiB
references/ai-assistant.md18.0 KiB
references/assessment-gotchas.md17.1 KiB
references/assessment-knowledge-retrieval.md9.3 KiB
references/assessment-shape-anti-pattern-pushback.md10.4 KiB
references/assessment-shape-comparative-decision.md12.9 KiB
references/assessment-shape-focused-operational.md9.9 KiB
references/assessment-shape-full-assessment.md20.7 KiB
references/assessment-shape-overview.md12.1 KiB
references/assessment-shape-schema-conversion.md8.9 KiB
references/assessment-shape-sizing-only.md9.4 KiB
references/assessment-shape-translation.md12.3 KiB
references/assessment-workflow.md48.4 KiB
references/compatibility-rubric.md5.9 KiB
references/log-analytics-guide.md18.0 KiB
references/log-analytics-osi-pipelines.md8.6 KiB
references/log-analytics-troubleshooting.md1.6 KiB
references/observability-ppl-reference.md15.1 KiB
references/observability.md14.5 KiB
references/personas.md10.4 KiB
references/provisioning-agentic-setup.md4.8 KiB
references/provisioning-domain-deploy-search.md2.2 KiB
references/provisioning-domain-provision.md3.4 KiB
references/provisioning-monitoring.md3.4 KiB
references/provisioning-reference.md6.8 KiB
references/provisioning-serverless-agentic-setup.md14.9 KiB
references/provisioning-serverless-deploy-search.md6.7 KiB
references/provisioning-serverless-deprovision.md4.0 KiB
references/provisioning-serverless-provision.md8.8 KiB
references/provisioning-storage-tiers.md2.1 KiB
references/provisioning-troubleshooting.md8.0 KiB
references/provisioning-upgrades.md5.4 KiB
references/readiness-rubric.md3.4 KiB
references/search-bedrock-connectors.md3.8 KiB
references/search-dense-vector-models.md5.5 KiB
references/search-document-processing-guide.md7.7 KiB
references/search-evaluation-guide.md5.3 KiB
references/search-index-config.md2.4 KiB
references/search-recipes.md12.9 KiB
references/search-semantic-search-guide.md18.2 KiB
references/search-sparse-vector-models.md4.9 KiB
references/search-troubleshooting.md1.9 KiB
references/security.md9.3 KiB
references/sizing.md12.2 KiB
references/source-elasticsearch.md6.7 KiB
references/source-opensearch.md4.8 KiB
references/trace-analytics-trace-ingestion.md3.3 KiB
references/trace-analytics-trace-queries.md13.9 KiB
references/trace-analytics-troubleshooting.md1.6 KiB
references/vector-knn.md12.9 KiB

references/source-elasticsearch.md

Elasticsearch Source Reference

Stable-core facts about Elasticsearch as a migration source to Amazon OpenSearch Service. Version-volatile details (exact OpenSearch minor that reaches parity, current MA version support floor/ceiling) MUST be tagged [verify] and resolved against live docs in Step 8 of the workflow.

ES version-family table

Use this to populate §1 "Recommended path" and §8 "Migration Plan" in the report.

ES version family	Fork status	Snapshot/Restore into AOS	Primary HDM strategy	Notes
ES 1.x / 2.x / 5.x	Pre-fork	NOT recommended (multi-major hop)	Migration Assistant Historical Data Migration	MA HDM supports source ES back to 1.0; multi-major hops require MA
ES 6.x	Pre-fork	Supported (pre-fork)	Snapshot/Restore OR MA HDM	Snapshot/Restore is the simpler path; MA HDM preferred for large/complex
ES ≤ 7.10.2	Pre-fork	Supported	Snapshot/Restore (maintenance window) OR MA HDM	Snapshot/Restore is the simplest path while license boundary allows
ES ≥ 7.11 (7.11–7.17, 8.x)	Post-fork ELv2/SSPL	BLOCKED (license lockout)	MA HDM (large/complex) or `_reindex` from remote (small, ≥30 min window)	Snapshot/Restore is architecturally blocked post-fork

Source/target version eligibility for each MA mode: see Migration Assistant source-and-target versions [verify].

ES → OpenSearch always-flag table

Every row below MUST be evaluated for every ES source migration. Copy confirmed findings into the gap register (elasticsearch-gap-register.md). Severity + Lane vocabulary from compatibility-rubric.md.

Feature	Elasticsearch behavior	OpenSearch alternative	Severity	Lane	Notes
Index Lifecycle Management (`_ilm/policy`)	ILM policy JSON	ISM (`_plugins/_ism/policies`) — policy JSON does NOT import	HIGH	risk-blocker	Rebuild each ILM policy as ISM; common patterns: rollover, force_merge, warm/cold, delete
X-Pack Watcher	Rule-based alerting	OpenSearch Alerting monitors + destinations	HIGH	risk-blocker	Rebuild monitors; smoke-test trigger conditions
Runtime fields (schema-on-read)	`runtime` mapping type	No equivalent	HIGH	risk-blocker	Pre-compute via ingest pipeline or scripted_field; reindex
Fleet / Elastic Agent	X-Pack ingest + endpoint management	No equivalent on AOS	BLOCKING	risk-blocker	Re-architect ingest on Data Prepper / OSI / Fluent Bit / OTel Collector
ELSER `text_expansion`	Elastic learned sparse retrieval (proprietary)	`neural_sparse` query + SageMaker-hosted sparse encoder	HIGH	risk-blocker	ELSER does not run on AOS; use neural_sparse or hybrid BM25+dense
`dense_vector` field	Dense vector + kNN	`knn_vector` (engine selection: see vector-knn.md)	MEDIUM	migration-specific	Pick engine (FAISS/Lucene/NMSLIB); reindex; validate recall
`_type` / multi-type mappings	ES 6.x multi-type or 7.x `_doc` placeholder	Types removed in OS 1.0; `_doc` placeholder OKs in 7.x but blows up `_reindex`	MEDIUM	migration-specific	MA metadata transformer flattens templates automatically
`fielddata: true` on text (ES 1.x/2.x)	In-memory fielddata for sort/agg	`.keyword` subfield + `doc_values`	BLOCKING	migration-specific	OOM risk on first aggregation; MA transformer strips fielddata and adds `.keyword` automatically
`_source: {enabled: false}`	`_source` not stored	Forces MA Historical Data Migration only — Snapshot/Restore cannot reconstruct	HIGH	risk-blocker	Use MA HDM; re-enable `_source` on target index
ES 8 `retriever` / `rrf`	Native reciprocal-rank fusion	Hybrid query + normalization-processor pipeline	HIGH	risk-blocker	Rebuild as hybrid search pipeline; benchmark ranking parity
Snapshot from ES ≥ 7.11	Snapshot archive	BLOCKED — ELv2/SSPL license lockout into AOS	BLOCKING	risk-blocker	Use MA HDM or `_reindex` from remote
Open Distro plugin names (`opendistro-*`)	`opendistro-*` plugin namespace	`opensearch-*` rename	LOW	migration-specific	Plugin namespace rename is mechanical; validate config files

ES field/mapping → OpenSearch table

Use as the audit checklist for §2 Schema/Mapping in the report and for elasticsearch-index-template-skeleton.md.

ES construct	OpenSearch equivalent	Action
`type: text` with `fielddata: true`	`type: text` + `.keyword` subfield	Strip fielddata; add keyword subfield
`type: flattened`	`type: flat_object`	Rename type
`type: dense_vector`	`type: knn_vector`	Change type + add engine/method parameters
`type: runtime` (runtime fields)	No equivalent	Pre-compute via ingest pipeline
Multi-type index (`_type`)	Single-type; `_type` removed	MA metadata transformer flattens automatically
`_source: {enabled: false}`	Supported but blocks Snapshot/Restore	Re-enable on target or use MA HDM
`index_patterns` (index template)	`index_patterns` (identical)	No change
`_ilm` lifecycle hooks in index settings	ISM policy attachment	Rewrite ILM → ISM; re-attach

ES API → OpenSearch API cheat-sheet

ES API	OpenSearch API	Notes
`GET /_ilm/policy`	`GET /_plugins/_ism/policies`	JSON format differs; rebuild required
`GET /_watcher/watch`	`GET /_plugins/_alerting/monitors`	Rebuild required
`GET /_xpack`	Not applicable	No X-Pack on AOS
`GET /_eql/search`	`GET /_plugins/ppl`	Use PPL for log analytics; EQL not available
`GET /_async_search`	`GET /_plugins/_asynchronous_search`	Semantics match; endpoint differs
`GET /_text_expansion` (ELSER)	`GET /_plugins/ml` (neural_sparse)	Model hosting required on AOS side

Always-true rules for ES sources

Post-fork snapshot lockout is architectural — do NOT recommend Snapshot/Restore for ES ≥ 7.11 under any circumstance.
MA HDM vs _reindex threshold — prefer _reindex from remote for post-fork ES when dataset is small and a ≥30 min maintenance window is available. MA HDM becomes primary for large/complex datasets or when source→target network reachability is not possible.
ILM → ISM is always a risk-blocker — there is no automated ILM import tool; every policy must be rebuilt.
ELSER is proprietary — do not promise ELSER functionality on AOS.
fielddata: true OOM risk — flag on every ES 1.x/2.x source even if MA handles it automatically.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

What our scanner noticed· Low risk

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Attribution and licence

Published by

AWS

Publisher

AWS (Official)

Source

aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/amazon-opensearch-service · commit 0a218f8

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
NOTICE (added by AutoGineer from NOTICE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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