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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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/amazon-opensearch-service/download
  2. 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.

CapabilityEntry-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.

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

references/assessment-shape-schema-conversion.md


case_shape: SCHEMA_CONVERSION purpose: Field-by-field mapping from a source schema (Solr schema.xml, Elasticsearch mapping, raw field list) to an OpenSearch index mapping when_to_use: "User pasted a schema artifact OR asked 'map these fields' / 'convert this schema' / 'what does <fieldType> become in OpenSearch'" NOT_for: Holistic readiness assessment (use FULL_ASSESSMENT), query syntax translation only (use TRANSLATION_TASK), justifying the choice of OpenSearch (use ANTI_PATTERN_PUSHBACK) length_target: 200-600 words plus the JSON mapping block

Recipe: SCHEMA_CONVERSION

Canonical reference. This is the canonical Solr-7-and-9 field-type-and-deprecation reference for the skill. Other files (assessment-workflow §X-Pack/Solr deprecation, assets/solr-gap-register, asset/report templates) link here for the exhaustive list.

1. Detection signals — dispatch here when

Trigger this shape when the user input contains any of the following. One strong signal is enough; do not require multiple.

  • Pasted XML containing <field name= or <fieldType name= or <schema name= (Solr schema.xml)

  • Pasted JSON containing "mappings", "properties", or "dynamic_templates" (ES/OS mapping export)

  • A flat list of field names with types like string, text_general, pdate, plong, TrieLong, EnumField, CurrencyField, solr.TextField

  • Imperative phrases: "map these fields", "convert this schema", "what's the OpenSearch equivalent of <type>", "translate this mapping"

  • File references: schema.xml, managed-schema, mapping.json, _mapping

If the user pasted a schema and asked sizing/readiness questions, dispatch SCHEMA_CONVERSION first, then offer to run FULL_ASSESSMENT as a follow-up. Do not silently merge shapes.

2. Required output template

Produce exactly these four sections in this order. Skip any section the user explicitly waived.

Section A — Field-by-field mapping table

A markdown table with columns: Source field | Source type | Target OpenSearch type | Mapping options | Notes. Every source field MUST appear with either a target mapping or the literal annotation omit — <reason>. No silent drops.

Section B — OpenSearch index mapping (JSON)

A complete, paste-ready PUT /<index> body containing mappings.properties and any required settings (analyzers, normalizers). Must be valid JSON; no ... ellipses, no // comments.

Section C — Special field bindings

Solr <uniqueKey> does not have a direct OpenSearch equivalent — _id is metadata, not a field. Show the binding three ways so the reader can pick the form that fits their pipeline:

  1. copy_to in the JSON mapping — keep the user's id field as a regular property and copy it where searches need it.

  2. Sample _bulk request — demonstrating the {"index":{"_id":"<value>"}} action line that pulls the id from the document at write time.

  3. Prose binding instruction — one sentence telling the indexer/ETL author to extract the source id field and place it in the action metadata.

Section D — Gap register

Bulleted list of every source field whose type is deprecated, removed, or has no direct OpenSearch equivalent. Always flag at minimum: TrieLong/TrieInt/TrieDate (deprecated since Solr 7, removed in Solr 9), EnumField (use keyword + application-side ordering), CurrencyField (split into scaled_float + keyword for currency code), solr.LatLonType (use geo_point), PreAnalyzedField (no equivalent — re-analyze at write time).

For the upstream Solr 9 upgrade-notes catalog (additions / removals / behavior changes), see Solr major changes.

Section E — Migration path callout (1-2 sentences)

State the recommended reindex strategy in one or two sentences: typically "logstash or a custom ETL reads from the Solr /select cursor and writes to OpenSearch _bulk; do a dual-write window if downtime is unacceptable." Do not expand this into a full migration plan.

A single line at the bottom: Reference: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/supported-operations.html (or a more specific page if relevant — field-data-types, analyzers, etc.). One URL. Not a list.

3. NOT REQUIRED — explicitly omit

Do NOT include any of the following in a SCHEMA_CONVERSION response:

  • Timeline & Resourcing — removed from the suite entirely. No engineer-weeks, no calendar estimates, no staffing.

  • Forced sizing section (no shard math, no instance picks, no storage calculations) — that is FULL_ASSESSMENT territory.

  • Readiness score / scorecard / traffic-light grid.

  • The 9-section assessment structure.

  • Customer-specific trade-offs ("you should consider X for your business" framing).

  • Pricing tables or cost estimates.

  • Standalone risks/blockers register beyond the gap-register deprecations in Section D — SCHEMA_CONVERSION ships only the gap-register, not the FULL_ASSESSMENT §7 two-table split.

If the user wants any of the above, tell them you'll switch shapes and ask for the inputs FULL_ASSESSMENT needs.

4. Worked exemplar

User: "Convert this Solr schema for OpenSearch."

<field name="product_id" type="string" indexed="true" stored="true" required="true"/>
<field name="title"      type="text_general" indexed="true" stored="true"/>
<field name="price"      type="TrieLong" indexed="true" stored="true"/>
<field name="created"    type="pdate" indexed="true" stored="true"/>
<field name="status"     type="EnumField" enumsConfig="enums.xml" enumName="status"/>
<uniqueKey>product_id</uniqueKey>

Response:

A. Field mapping

Source fieldSource typeTarget OS typeOptionsNotes
product_idstringkeywordcopy_to: ["_id_src"]Bound to _id — see Section C
titletext_generaltextanalyzer: standard, sub-field title.keyword of type keywordMulti-field for sort/agg
priceTrieLonglong—deprecated since Solr 7, removed in Solr 9 — straight long replacement
createdpdatedateformat: strict_date_optional_time
statusEnumFieldkeyword—OpenSearch has no enum; use keyword and order at app layer

B. JSON mapping

PUT /products
{
  "mappings": {
    "properties": {
      "product_id": { "type": "keyword" },
      "title":      { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } },
      "price":      { "type": "long" },
      "created":    { "type": "date", "format": "strict_date_optional_time" },
      "status":     { "type": "keyword" }
    }
  }
}

C. uniqueKey → _id binding (three forms)

  1. copy_to in JSON — already shown: keep product_id as a keyword field and reference it in your indexer.

  2. Sample _bulk —

    POST /_bulk
    { "index": { "_index": "products", "_id": "SKU-12345" } }
    { "product_id": "SKU-12345", "title": "Wireless mouse", "price": 2999, "created": "2026-05-30T12:00:00Z", "status": "ACTIVE" }
  3. Prose — your ETL must read product_id from each Solr document and place its value in the _id field of the bulk action metadata; OpenSearch will reject documents whose action line omits _id only if you require deterministic ids.

D. Gap register

  • TrieLong — deprecated Solr 7, removed Solr 9 → long

  • EnumField — no OpenSearch equivalent → keyword + application-side ordering

E. Migration path — Use Logstash with the solr input plugin or a cursor-based ETL writing to _bulk; run a dual-write window during cutover if zero downtime is required.

F. Reference: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/supported-operations.html

5. Pre-emit checklist (tick before responding)

Tick each box. If any box cannot be ticked, fix the response before sending.

  • [ ] Every source field appears in the mapping table (no silent drops; explicit omit — reason if dropped).

  • [ ] JSON mapping block is valid, paste-ready, no ... or comments.

  • [ ] <uniqueKey> (or equivalent id field) is shown in all three binding forms: copy_to JSON,_bulk sample, prose.

  • [ ] At least one row in the gap register is labeled with deprecation provenance (e.g., "deprecated since Solr 7") if a deprecated type appears in the source.

  • [ ] TrieLong / TrieInt / TrieDate rows, if present, are explicitly labeled deprecated.

  • [ ] Exactly one doc URL footer at the bottom — not a list.

  • [ ] Migration path callout is 1-2 sentences, not a plan.

  • [ ] No Timeline & Resourcing section. No engineer-weeks. No calendar estimates.

  • [ ] No readiness score, no forced sizing, no 9-section structure.

  • [ ] Response is within the 200-600 word target (excluding the JSON block).

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

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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)
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