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://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/trace-analytics-trace-queries.md

Trace-analytics capability — entry point and query templates

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

This file is the entry point for the trace-analytics capability. It covers distributed traces with OpenTelemetry — span queries, service maps, latency analysis (p50/p95/p99), error rate by service, and root-cause via parent/child spans.

When to use this capability

SKILL.md routes here when the user is working with distributed traces on AOS / AOSS. Concrete triggers:

Phrases: "trace analytics", "service map", "otel", "distributed traces", "span query", "otel-v1-apm-span-"*, "Data Prepper", "latency p99"
Tasks: query trace spans, build service maps, ingest traces (OTel collector → Data Prepper / OSI), troubleshoot trace pipeline or query issues

All trace-analytics files (capability index)

User need	File
Span queries (PPL on `otel-v1-apm-span-*`)	this file
Trace ingestion (OTel collector → Data Prepper / OSI)	`trace-analytics-trace-ingestion.md`
Troubleshoot trace pipeline or queries	`trace-analytics-troubleshooting.md`

Cross-cutting refs you may also load: security.md, personas.md (observability-engineer).

Cross-capability handoff

For log queries on the same domain: see log-analytics-guide.md.
For provisioning the trace-collector infra (Data Prepper / OSI / IAM): see provisioning-reference.md.
For OSI pipeline configuration shared with logs: see log-analytics-osi-pipelines.md.

Query discipline (MUST follow)

For the full PPL command and function catalog, see observability-ppl-reference.md.

Unknown command → upstream grammar. If a PPL command or function is not in observability-ppl-reference.md, or an emitted query fails with a syntax error, you MUST fetch the raw upstream doc from opensearch-project/sql under docs/user/ppl/ before answering. You MUST NOT invent PPL syntax, because OpenSearch PPL has version-specific commands that do not exist in other query languages.
Verify or disclose. When the domain/collection endpoint is reachable, you MUST validate every emitted query against _plugins/_ppl; on 0 rows, fall back to _plugins/_ppl/_explain to confirm the plan and surface the empty result; on error, fix and re-validate. When no endpoint is reachable, you MUST state that the query is unverified, because an untested query can silently reference a non-existent field.

Data Plane Access with awscurl

All queries below use the PPL API at /_plugins/_ppl. Use awscurl for SigV4-authenticated requests:

Credentials & monitoring: source the SigV4 signing credentials from an IAM role (instance profile, ECS task role, or SSO session), not static access keys. Enable CloudTrail for management-plane audit and OpenSearch audit logs for data-plane access (to attribute PPL queries to callers), and alarm on _plugins/_ppl error rates.

Base Command (AOS)

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "<PPL_QUERY>"}'

Base Command (AOSS)

awscurl --service aoss --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "<PPL_QUERY>"}'

Prerequisites: pip install awscurl, AWS credentials configured via aws configure or environment variables.

Verifying Trace Indices

awscurl --service es --region $AWS_REGION \
  "$OPENSEARCH_ENDPOINT/_cat/indices/otel-v1-apm-*?v&h=index,health,docs.count,store.size"

Sampling Recent Spans

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{"size": 5, "sort": [{"startTime": "desc"}], "query": {"match_all": {}}}'

Trace Index Key Fields

Field	Type	Description
`traceId`	keyword	Unique 128-bit trace identifier
`spanId`	keyword	Unique 64-bit span identifier
`parentSpanId`	keyword	Parent span ID (empty for root spans)
`serviceName`	keyword	Service that produced the span
`name`	keyword	Span operation name
`kind`	keyword	Span kind (SPAN_KIND_SERVER, SPAN_KIND_CLIENT, SPAN_KIND_INTERNAL, SPAN_KIND_PRODUCER, SPAN_KIND_CONSUMER)
`startTime`	date	Span start timestamp
`endTime`	date	Span end timestamp
`durationInNanos`	long	Span duration in nanoseconds
`status.code`	integer	0=Unset, 1=Ok, 2=Error
`attributes.gen_ai.operation.name`	keyword	GenAI operation type
`attributes.gen_ai.agent.name`	keyword	Agent name
`attributes.gen_ai.agent.id`	keyword	Agent identifier
`attributes.gen_ai.request.model`	keyword	Requested model
`attributes.gen_ai.usage.input_tokens`	long	Input token count
`attributes.gen_ai.usage.output_tokens`	long	Output token count
`attributes.gen_ai.tool.name`	keyword	Tool name
`attributes.gen_ai.tool.call.id`	keyword	Tool call identifier
`attributes.gen_ai.tool.call.arguments`	text	Tool call arguments (JSON)
`attributes.gen_ai.tool.call.result`	text	Tool call result (JSON)
`attributes.gen_ai.conversation.id`	keyword	Conversation identifier
`attributes.error_type`	keyword	Error type category
`events.attributes.exception.type`	keyword	Exception class/type
`events.attributes.exception.message`	text	Exception message
`events.attributes.exception.stacktrace`	text	Exception stacktrace

GenAI Operation Types

Operation	Description
`invoke_agent`	Top-level agent invocation
`execute_tool`	Tool execution within agent reasoning
`chat`	LLM chat completion call
`embeddings`	Text embedding generation
`retrieval`	Retrieval operation (e.g., RAG)
`create_agent`	Agent creation/initialization
`text_completion`	Text completion (non-chat)
`generate_content`	Generic content generation

PPL Query Templates

Usage: Replace <PPL_QUERY> in the base command above with any query below. Example:

awscurl --service es --region us-east-1 \
  -X POST "https://my-domain.us-east-1.es.amazonaws.com/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where `attributes.gen_ai.operation.name` = '\''invoke_agent'\'' | head 20"}'

Agent Invocation Spans

source = otel-v1-apm-span-* | where `attributes.gen_ai.operation.name` = 'invoke_agent' | fields traceId, spanId, `attributes.gen_ai.agent.name`, `attributes.gen_ai.request.model`, durationInNanos, startTime | sort - startTime | head 20

Tool Execution Spans

source = otel-v1-apm-span-* | where `attributes.gen_ai.operation.name` = 'execute_tool' | fields traceId, spanId, `attributes.gen_ai.tool.name`, durationInNanos, startTime | sort - startTime | head 20

Slow Spans

Default threshold: 5 seconds (5,000,000,000 nanoseconds). Adjust as needed.

source = otel-v1-apm-span-* | where durationInNanos > 5000000000 | fields traceId, spanId, serviceName, name, durationInNanos, startTime | sort - durationInNanos | head 20

Error Spans

status.code = 2 means ERROR in OTel:

source = otel-v1-apm-span-* | where `status.code` = 2 | fields traceId, spanId, serviceName, name, `status.code`, startTime | sort - startTime | head 20

Token Usage by Model

source = otel-v1-apm-span-* | where `attributes.gen_ai.usage.input_tokens` > 0 | stats sum(`attributes.gen_ai.usage.input_tokens`) as total_input, sum(`attributes.gen_ai.usage.output_tokens`) as total_output by `attributes.gen_ai.request.model`

Token Usage by Agent

source = otel-v1-apm-span-* | where `attributes.gen_ai.usage.input_tokens` > 0 | stats sum(`attributes.gen_ai.usage.input_tokens`) as total_input, sum(`attributes.gen_ai.usage.output_tokens`) as total_output by `attributes.gen_ai.agent.name`

Service Operations Listing

source = otel-v1-apm-span-* | stats count() by serviceName, `attributes.gen_ai.operation.name`

Trace Tree Reconstruction

source = otel-v1-apm-span-* | where traceId = '<TRACE_ID>' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime

Root Span Identification

source = otel-v1-apm-span-* | where traceId = '<TRACE_ID>' AND parentSpanId = '' | fields traceId, spanId, serviceName, name, durationInNanos, startTime, endTime

Spans with Exceptions

source = otel-v1-apm-span-* | where `status.code` = 2 | fields traceId, spanId, serviceName, name, `events.attributes.exception.type`, `events.attributes.exception.message`, `attributes.error_type`, startTime | sort - startTime | head 20

Conversation Tracking

source = otel-v1-apm-span-* | where `attributes.gen_ai.conversation.id` != '' | stats count() as turns, sum(`attributes.gen_ai.usage.input_tokens`) as total_input_tokens, sum(`attributes.gen_ai.usage.output_tokens`) as total_output_tokens by `attributes.gen_ai.conversation.id`

Tool Call Inspection

source = otel-v1-apm-span-* | where `attributes.gen_ai.operation.name` = 'execute_tool' | fields traceId, spanId, `attributes.gen_ai.tool.name`, `attributes.gen_ai.tool.call.id`, `attributes.gen_ai.tool.call.arguments`, `attributes.gen_ai.tool.call.result`, durationInNanos, startTime | sort - startTime | head 20

Service Map Queries

Important: In otel-v2-apm-service-map-*, sourceNode and targetNode are nested struct objects with keyAttributes.name for the service name — not flat strings.

Service Topology

source = otel-v2-apm-service-map-* | dedup nodeConnectionHash | fields sourceNode, targetNode, sourceOperation, targetOperation

Remote Service Identification with coalesce()

Different OTel instrumentation libraries use different attributes. Use coalesce() to check multiple fields:

source = otel-v1-apm-span-* | where serviceName = 'frontend' | where kind = 'SPAN_KIND_CLIENT' | eval _remoteService = coalesce(`attributes.net.peer.name`, `attributes.server.address`, `attributes.rpc.service`, `attributes.db.system`, `attributes.gen_ai.system`, 'unknown') | stats count() as calls by _remoteService | sort - calls

Query DSL Examples (awscurl)

For complex aggregations that PPL doesn't support well, use Query DSL with awscurl:

Latency Percentiles by Service

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "size": 0,
  "query": {"range": {"startTime": {"gte": "now-1h"}}},
  "aggs": {
    "by_service": {
      "terms": {"field": "serviceName", "size": 20},
      "aggs": {
        "latency_percentiles": {
          "percentiles": {
            "field": "durationInNanos",
            "percents": [50, 90, 95, 99]
          }
        }
      }
    }
  }
}'

Error Rate by Service

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "size": 0,
  "query": {"range": {"startTime": {"gte": "now-1h"}}},
  "aggs": {
    "by_service": {
      "terms": {"field": "serviceName", "size": 20},
      "aggs": {
        "total": {"value_count": {"field": "spanId"}},
        "errors": {
          "filter": {"term": {"status.code": 2}},
          "aggs": {
            "count": {"value_count": {"field": "spanId"}}
          }
        }
      }
    }
  }
}'

Throughput Over Time

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "size": 0,
  "query": {"range": {"startTime": {"gte": "now-1h"}}},
  "aggs": {
    "over_time": {
      "date_histogram": {
        "field": "startTime",
        "fixed_interval": "5m"
      },
      "aggs": {
        "by_service": {
          "terms": {"field": "serviceName", "size": 10}
        }
      }
    }
  }
}'

Slow Operations (P99 > 1s)

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "size": 0,
  "aggs": {
    "by_operation": {
      "terms": {"field": "name", "size": 50},
      "aggs": {
        "p99_latency": {
          "percentiles": {
            "field": "durationInNanos",
            "percents": [99]
          }
        },
        "high_latency": {
          "bucket_selector": {
            "buckets_path": {"p99": "p99_latency.99"},
            "script": "params.p99 > 1000000000"
          }
        }
      }
    }
  }
}'

Find Spans by Service (DSL)

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "query": {
    "bool": {
      "must": [
        {"term": {"serviceName": "ORDER_SERVICE"}},
        {"range": {"startTime": {"gte": "now-1h"}}}
      ]
    }
  },
  "sort": [{"startTime": "desc"}],
  "size": 20
}'

Get Full Trace by ID (DSL)

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v1-apm-span-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{
  "query": {"term": {"traceId": "TRACE_ID_HERE"}},
  "sort": [{"startTime": "asc"}],
  "size": 100
}'

Service Map (DSL)

awscurl --service es --region $AWS_REGION \
  -X POST "$OPENSEARCH_ENDPOINT/otel-v2-apm-service-map-*/_search" \
  -H 'Content-Type: application/json' \
  -d '{"size": 200, "query": {"match_all": {}}}'

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)

Synced

2026-11-09

Also distributed in

aws/agent-toolkit-for-aws

AutoGineer is not affiliated with or endorsed by AWS or AWS.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download amazon-opensearch-service.zip