
Amazon OpenSearch Service
OfficialFreeStreamline your OpenSearch and log analytics workflows.
Free · Opens the source repo
What Amazon OpenSearch Service does
The Amazon OpenSearch Service skill provides a comprehensive toolset for managing and utilizing Amazon OpenSearch Service (AOS) and Amazon OpenSearch Serverless (AOSS). It encompasses five key capabilities: migration, provisioning, search, log analytics, and trace analytics. Users can leverage this skill to migrate from Solr or Elasticsearch to AOS/AOSS, manage domain lifecycles, perform advanced search queries, analyze logs, and trace distributed applications. Each capability is designed to handle specific tasks efficiently, ensuring that users can focus on their core objectives without getting bogged down in the complexities of the underlying infrastructure.
For migration, the skill supports schema and query translation, sizing, and cutover processes, allowing users to transition smoothly from self-managed systems to AOS/AOSS. The provisioning capability simplifies the management of AOS domains and AOSS collections, covering lifecycle management, upgrades, storage tiers, and monitoring. Users can also implement advanced search techniques, including vector and semantic search, utilizing Bedrock connectors and hybrid retrieval methods.
Log analytics is another critical feature, providing tools for observability, anomaly detection, and integration with OpenSearch Dashboards. This capability serves as an alternative to traditional logging solutions like Splunk and Datadog, enabling users to efficiently analyze and visualize their log data. Lastly, trace analytics allows for the collection and analysis of distributed traces using OpenTelemetry, helping users understand service interactions and performance.
Overall, this skill is designed for developers and DevOps engineers who need to streamline their OpenSearch and log analytics workflows, making it a valuable addition to their toolkit.
When to use it
Use this skill when you need to migrate to Amazon OpenSearch Service, manage your OpenSearch domains, or analyze logs and traces effectively.
When not to use it
This skill may not be suitable for users looking for basic search functionalities without the need for advanced analytics or migration capabilities.
What you can build with it
Migrating from Elasticsearch
Use this skill to facilitate the migration process from Elasticsearch to Amazon OpenSearch Service, ensuring schema compatibility and efficient data transfer.
Setting Up Log Analytics
Leverage the log analytics capability to analyze and visualize log data using OpenSearch Dashboards, providing insights into system performance.
Implementing Advanced Search
Utilize the search capabilities to set up vector and semantic search within your applications, enhancing user experience with advanced retrieval techniques.
How to install Amazon OpenSearch Service
View source1. Install with the skills CLI
npx skills add aws/agent-toolkit-for-aws/amazon-opensearch-service --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by awsAmazon OpenSearch Service — the unified skill
This skill answers anything about Amazon OpenSearch Service or Serverless across five 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, itscall_awstool offers a streamlined alternative — but every operation in this skill MUST work via the AWS CLI alone. Data-plane HTTP calls against AOS / AOSS useawscurlfor SigV4-signed requests; this works in both contexts.
Step 0: detect the capability — first thing you do
Pick one of the five 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 |
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 thecurrent_timetool (returns ISO 8601 in UTC). Read the skill version from this file's frontmatterversion: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 aCategory: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
big5workload + your own judgment list.
Frequently asked questions about Amazon OpenSearch Service
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