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/provisioning-troubleshooting.md

Troubleshooting AOS Domains and Collections

The AWS MCP server is recommended for executing these commands but is not required — all steps use standard AWS CLI syntax.

Common Issues

Error	Cause	Fix
`ValidationException` on create	Invalid config combination	Check instance type supports chosen EBS volume; verify AZ count matches instance count
Domain stuck in `Processing`	Blue/green in progress	Wait; check `describe-domain-change-progress` for stage details
`ResourceAlreadyExistsException`	Domain name taken in account	Choose a different name; domain names must be unique per account per region
Upgrade fails at pre-checks	Incompatible settings or plugins	Run `get-compatible-versions`; address breaking changes listed in upgrade guide
`DisabledOperationException`	Operation not available for config	Some operations (cold storage, UltraWarm) require specific instance families
Snapshot failure	S3 bucket permissions or IAM role	Verify snapshot role has `s3:PutObject` on the bucket; check trust policy

Serverless (AOSS) provisioning & teardown errors

Error	Cause	Fix
`ConflictException` / "already exists" on create	Resource name taken in this account+region	Choose a new name, because collection/policy names must be unique per account per region
`Invalid value for maxIndexingCapacityInOCU`	OCU value outside the allowed set	Set a valid OCU value per serverless-scaling.html, because AOSS rejects capacity numbers outside the allowed set
"Providing VectorOptions ... not supported for account"	Account lacks vector acceleration	Retry without `vectorOptions`, because acceleration needs account-level enablement
`ConflictException` on `delete-collection` (status not ACTIVE/FAILED)	Collection still creating	Poll `batch-get-collection` until `status` is `ACTIVE`/`FAILED`, then retry, because a mid-creation collection cannot be deleted
`ValidationException` "has collections associated" on `delete-collection-group`	Group still holds collections	Delete/await member collections first, because a non-empty group cannot be removed
Partial-failure mid-provision (group created, collection failed)	One step failed after earlier steps succeeded	Report what exists, then use the deprovision flow to clean up, because orphaned policies/groups still bill and block name reuse

For the full teardown ordering, see provisioning-serverless-deprovision.md.

Debugging Domain Creation Failures

Check domain status: aws opensearch describe-domain --domain-name <name>
Look for ServiceSoftwareOptions — may indicate pending mandatory updates
Verify service-linked role exists: aws iam get-role --role-name AWSServiceRoleForAmazonOpenSearchService
If VPC: verify subnet has available IPs, security group allows port 443

Debugging Blue/Green Stuck

aws opensearch describe-domain-change-progress --domain-name <name>
Check if cluster is red (blue/green won't complete with red cluster)
Verify sufficient capacity in the AZ for the new configuration
Common blocker: snapshot in progress — wait for it to complete

Debugging Auto-Tune

Check state: aws opensearch describe-domain-config --domain-name <name> --query 'DomainConfig.AutoTuneOptions'
Auto-Tune requires: domain running OpenSearch 1.0+, instance types with >= 4 GiB RAM
Recommendations are applied during maintenance windows only

High JVM pressure / RED cluster / unassigned shards

The canonical playbook for "JVMMemoryPressure is at 9X%, cluster is RED, shards are unassigned" on a provisioned domain.

Math first — get the per-node shard count right

Be exact. Don't average across "what could fit" if some nodes are already capped:

shards_per_node_actual = total_shards ÷ live_data_nodes        (if shards balance perfectly)
shards_per_node_cap    = 1000 × (heap_GiB ÷ 16)                (OS ≥ 2.17 rule, capped at 4000)
                       = 25 × heap_GiB                           (legacy "safe target")

Worked example for the typical case (3 × r7g.2xlarge.search data nodes):

Per r7g.2xlarge.search: 64 GiB RAM → 32 GiB JVM heap (50% rule, 32 GiB cap)
Per-node shard cap (OS ≥ 2.17): 32 ÷ 16 × 1000 = 2000 shards/node (hard ceiling)
Per-node "safe" target: 25 × 32 = 800 shards/node
4500 shards across 3 nodes = 1500 shards/node (assuming even distribution) — under the 2000 hard cap, well over the 800 safe target. Heap pressure expected at this density.

When writing the assessment: do the division once, present the single number (1500 shards/node), then compare it against BOTH the hard cap and the safe target. Do NOT present 750 shards/node somewhere and 1500 shards/node later in the same response — the reader loses trust.

The actual fix order (do these in this sequence)

Step 1 — Stabilize the heap before changing topology. Identify shards in flight (recovery, force-merge); throttle or pause them. Check field-data circuit breaker and clear unused field caches. Reduce indexing pressure (lower client-side bulk concurrency); rolling restart NOT advised at >85% pressure.

Step 2 — Resolve unassigned shards (gets cluster out of RED). Identify each unassigned shard's reason via _cat/shards?h=index,shard,prirep,state,unassigned.reason:

Replicas unassigned because of allocation rules (most common): force allocation if a node has slots, OR temporarily reduce replica count for the affected non-critical indices to 0 — but ONLY for indices you can afford to lose if a node fails AND with an explicit "this is destructive availability tradeoff" callout. Re-raise replicas after consolidation.
Primaries unassigned: do NOT touch replicas. The data itself is at risk. Add a node before doing anything else.

Replica-drop is a LAST-RESORT availability tradeoff, not Step 1 of a runbook. Always frame it as: "This drops fault tolerance on these indices until we re-replicate. Acceptable only if data loss on a single-node failure is tolerable for the X-hour recovery window." Without that framing the recommendation reads as casual destructiveness.

Step 3 — Reduce shard overhead permanently (the actual fix to the JVM-pressure root cause):

Use _shrink to consolidate over-sharded write-once indices: target 30–50 GiB shard size, not the default 5-shard template that produced this problem.
Use _rollover (or ISM-managed rollover) to retire write indices at a sane size threshold instead of letting them accumulate. New indices use the consolidated shard count.
For time-series, set up an ISM policy: hot rollover at 50 GB or 1 day → warm at 7d → delete at 90d. ISM is the load-bearing operational fix; the runbook should name _rollover, _shrink, and ISM explicitly.
Identify high-shard-count indices: _cat/indices?v&s=pri:desc,index | head -30 — usually a handful of indices dominate.
Close or delete unused indices to free shard slots immediately.

Step 4 — Add capacity (only after Step 3 is in flight). Scale 3 → 6 nodes via blue/green to redistribute shards and halve per-node density. Adding nodes before consolidating shards just delays the same problem.

Disk watermark trio (cite alongside JVM pressure when relevant)

OpenSearch disk watermarks (defaults): cluster.routing.allocation.disk.watermark.low = 85% (no new shard allocations to this node), high = 90% (existing shards relocate off this node), flood_stage = 95% (index goes read-only — all writes blocked, recovery is a manual ack). High JVM and high disk often arrive together; both must be addressed.

Pressure thresholds (what triggers the write-block)

Write-block trigger: JVMMemoryPressure > 92% for 30 consecutive minutes.
Write-block release: JVMMemoryPressure ≤ 88% for 5 minutes.
At 91% with shard pressure, you are one spike away from the block. The runbook MUST cite the 92%/30-min threshold.

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

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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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