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Amazon Opensearch Service

AWSOfficialPublished by AWS

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
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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://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/search-recipes.md

Search recipes — query DSL for app developers

The summary is in SKILL.md (§ Build a search feature). This file owns the recipes — copy-paste DSL for every common search pattern.

Index design 101

Always define a mapping before first ingest. Dynamic mapping creates bloated text + keyword multi-fields you'll regret.

Standard mapping for a search-driven app
PUT my-app
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "default": { "type": "standard" },
        "english_with_synonyms": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "stop", "english_stemmer", "synonyms_filter"]
        }
      },
      "filter": {
        "english_stemmer": { "type": "stemmer", "language": "english" },
        "synonyms_filter": {
          "type": "synonym",
          "synonyms": [
            "tv, television",
            "couch, sofa, settee"
          ]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "id":          { "type": "keyword" },
      "title":       { "type": "text", "analyzer": "english_with_synonyms", "fields": { "keyword": { "type": "keyword" }, "completion": { "type": "search_as_you_type" } } },
      "description": { "type": "text", "analyzer": "english_with_synonyms" },
      "tags":        { "type": "keyword" },
      "category":    { "type": "keyword" },
      "price":       { "type": "scaled_float", "scaling_factor": 100 },
      "in_stock":    { "type": "boolean" },
      "released_at": { "type": "date" },
      "rating":      { "type": "half_float" }
    }
  }
}

Key choices:

  • text for fields you search; keyword for facets/sort/exact-match

  • Multi-fields "title": {"type":"text", "fields": {"keyword": {"type":"keyword"}}} to support both

  • search_as_you_type for autocomplete

  • scaled_float for currency (better than float for known precision)

  • Avoid nested unless you actually need it — it's expensive

Full-text search

Single-field match
GET my-app/_search
{
  "query": { "match": { "title": "wireless headphones" } }
}
Multi-field with field boosting
GET my-app/_search
{
  "query": {
    "multi_match": {
      "query": "wireless headphones",
      "type": "best_fields",
      "fields": ["title^3", "description^1", "tags^2"]
    }
  }
}

type options:

  • best_fields (default) — score = highest single-field score (good for unique-content queries)

  • most_fields — score = sum of all matching fields (good when same content in multiple fields)

  • cross_fields — treats fields as one big field (good for entity searches like "first_name last_name")

  • phrase — must match as phrase

  • phrase_prefix — phrase + last token can be a prefix

Boolean (combine queries)
GET my-app/_search
{
  "query": {
    "bool": {
      "must":     [{ "match": { "title": "headphones" } }],
      "should":   [{ "match": { "tags": "noise-cancelling" } }],
      "filter":   [{ "term": { "in_stock": true } }, { "range": { "price": { "lte": 200 } } }],
      "must_not": [{ "term": { "category": "discontinued" } }]
    }
  }
}

filter doesn't affect score and is cached — use for non-relevance constraints (in-stock, price range, category).

Phrase queries
{ "match_phrase": { "title": { "query": "machine learning", "slop": 1 } } }

slop=N allows N word movements within the phrase.

Operator override (Solr q.op=AND equivalent)

OpenSearch defaults to OR. To replicate Solr's q.op=AND:

{
  "query": {
    "match": {
      "title": {
        "query": "wireless headphones bluetooth",
        "operator": "AND"
      }
    }
  }
}

Or for query_string:

{ "query_string": { "query": "wireless AND headphones", "default_operator": "AND" } }

This is the most common cause of result divergence when migrating from Solr.

Faceted search (aggregations)

GET my-app/_search
{
  "size": 20,
  "query": { "match": { "title": "headphones" } },
  "aggs": {
    "by_category":  { "terms": { "field": "category", "size": 10 } },
    "by_brand":     { "terms": { "field": "tags", "size": 10 } },
    "price_ranges": {
      "range": {
        "field": "price",
        "ranges": [
          { "to": 50 },
          { "from": 50, "to": 100 },
          { "from": 100, "to": 200 },
          { "from": 200 }
        ]
      }
    },
    "avg_rating":   { "avg": { "field": "rating" } }
  }
}

Multi-select facets (a user clicked one filter but should still see counts for other facets):

  • Use post_filter for the clicked facet so other aggs still see all matches

  • Use a filter aggregation per facet to apply ALL filters EXCEPT this one

Autocomplete / search-as-you-type

Option 1: search_as_you_type field
{
  "query": {
    "multi_match": {
      "query": "wirele",
      "type": "bool_prefix",
      "fields": ["title.completion", "title.completion._2gram", "title.completion._3gram"]
    }
  }
}

Best for general autocomplete on existing fields.

Option 2: completion suggester
PUT my-app/_doc/1
{
  "title": "Sony WH-1000XM5 Wireless Headphones",
  "title_completion": {
    "input": ["Sony WH-1000XM5", "Sony Wireless Headphones", "Noise Cancelling"]
  }
}

POST my-app/_search
{
  "suggest": {
    "title_suggest": {
      "prefix": "wirele",
      "completion": { "field": "title_completion", "size": 5 }
    }
  }
}

Best for product/entity name autocomplete with curated alternatives.

Option 3: edge_ngram (legacy / rarely needed)

For non-native scripts where prefix matters character-by-character.

Spell correction (Did You Mean)

{
  "suggest": {
    "spell_check": {
      "text": "wirless headfones",
      "phrase": {
        "field": "title",
        "size": 1,
        "gram_size": 3,
        "direct_generator": [{
          "field": "title",
          "suggest_mode": "always"
        }],
        "highlight": { "pre_tag": "<em>", "post_tag": "</em>" }
      }
    }
  }
}

Fuzzy search (typo tolerance)

{
  "query": {
    "match": {
      "title": {
        "query": "wireles",
        "fuzziness": "AUTO"
      }
    }
  }
}

fuzziness: AUTO (recommended): 0 edits for ≤2 char terms, 1 edit for 3–5 chars, 2 edits for ≥6 chars.

"More like this" / similar items

{
  "query": {
    "more_like_this": {
      "fields": ["title", "description"],
      "like": [{ "_index": "my-app", "_id": "12345" }],
      "min_term_freq": 1,
      "max_query_terms": 12
    }
  }
}

Function score (custom relevance)

Boost recent items, popular items, in-stock items:

{
  "query": {
    "function_score": {
      "query": { "match": { "title": "headphones" } },
      "functions": [
        {
          "filter": { "term": { "in_stock": true } },
          "weight": 1.5
        },
        {
          "field_value_factor": {
            "field": "rating",
            "factor": 0.5,
            "modifier": "log1p",
            "missing": 0
          }
        },
        {
          "gauss": {
            "released_at": {
              "origin": "now",
              "scale": "30d",
              "decay": 0.5
            }
          }
        }
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

Sorting

{
  "query": { "match": { "title": "headphones" } },
  "sort": [
    { "rating": { "order": "desc" } },
    { "price":  { "order": "asc" } },
    "_score"
  ]
}

To sort on a text field, sort on its .keyword subfield.

Highlighting

{
  "query": { "match": { "title": "headphones" } },
  "highlight": {
    "fields": {
      "title":       { "pre_tags": ["<em>"], "post_tags": ["</em>"] },
      "description": { "fragment_size": 150, "number_of_fragments": 3 }
    }
  }
}

Pagination

Standard from/size (works up to ~10K results)
{ "from": 100, "size": 20, "query": { "match_all": {} } }
search_after for deep pagination
{
  "size": 20,
  "query": { "match_all": {} },
  "sort": [{ "_id": "asc" }],
  "search_after": ["last_doc_id_from_previous_page"]
}
point_in_time (PIT) for consistent paging across long sessions
POST my-app/_search/point_in_time?keep_alive=1m
# returns "pit_id"

POST _search
{
  "size": 20,
  "pit": { "id": "<pit_id>", "keep_alive": "1m" },
  "sort": [{ "_id": "asc" }],
  "search_after": [...]
}

Synonyms

Index-time synonyms (slower indexing, faster queries, larger index)

Define in mapping analysis.filter.synonyms_filter and apply analyzer to text fields.

Search-time synonyms (faster indexing, slower queries, smaller index)
{
  "settings": {
    "analysis": {
      "filter": {
        "search_synonyms": {
          "type": "synonym_graph",
          "synonyms": ["tv, television", "couch, sofa"]
        }
      },
      "analyzer": {
        "search_with_synonyms": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "search_synonyms"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "standard",
        "search_analyzer": "search_with_synonyms"
      }
    }
  }
}

Recommendation: Start with search-time synonyms — easier to update without reindexing.

Boost recent items in relevance

Use function_score with gauss decay (above) — natural log decay over time.

Geo search

{
  "query": {
    "bool": {
      "must": { "match": { "name": "coffee" } },
      "filter": {
        "geo_distance": {
          "distance": "5km",
          "location": { "lat": 47.6062, "lon": -122.3321 }
        }
      }
    }
  }
}

Field type: geo_point or geo_shape.

Solr → OpenSearch query translation reference

SolrOpenSearch DSL
q=headphones{"multi_match": {"query": "headphones", "fields": ["title", "description"]}} (no _all in OpenSearch — list fields explicitly)
q=title:headphones{"match": {"title": "headphones"}}
q.op=AND"default_operator": "AND" on query_string OR "operator": "AND" on match
qf=title^3 description (eDisMax)multi_match type: best_fields with fields: ["title^3", "description"]
pf=title^5 (phrase boost)should clause with multi_match type:phrase (approximation only)
tie=0.3 (eDisMax)tie_breaker: 0.3 on multi_match type: best_fields
mm=2<-25%minimum_should_match: "2<-25%" (passes UNCHANGED)
fq=in_stock:truefilter clause in bool query: {"term": {"in_stock": true}}
sort=rating desc, price ascsort: [{"rating": "desc"}, {"price": "asc"}]
start=20&rows=20from: 20, size: 20
facet=true&facet.field=categoryaggs: {"by_category": {"terms": {"field": "category"}}}
hl=true&hl.fl=titlehighlight: {"fields": {"title": {}}}
mlt=truemore_like_this query
bf=recip(...) (boost function)function_score with field_value_factor
defType=edismaxmulti_match (closest equivalent)
wt=jsonAccept: application/json header (OpenSearch defaults JSON)

Common gotchas

  1. Dynamic mapping — first doc creates field types. A field like "id": "12345" becomes text (not keyword) and text can't be used for sort/facet without fielddata: true (OOM-prone). Always pre-define mappings.

  2. Cannot change field type without reindex. Add new field, dual-write, switch reads, drop old.

  3. text vs keyword — text is analyzed (lowercased, tokenized, stemmed). Keyword is stored as-is. For an ID field that should be exact-match, use keyword.

  4. refresh_interval is 1s default. New documents not searchable for up to 1s. Force with ?refresh=true (slow — use sparingly).

  5. _id is automatic by default (random UUID). Set explicit _id in _bulk to ensure idempotent writes.

  6. max_result_window defaults to 10,000. To page beyond, use search_after or point_in_time. Don't blindly raise the setting.

  7. Aggregations on text fields require fielddata: true (OOM risk). Use keyword subfields for aggs/sort.

  8. null ≠ missing — explicitly handle null with "null_value" in mapping or use exists query.

  9. Reserved field names like _id, _source, _index, _doc. Don't try to redefine them.

  10. copy_to is the OpenSearch native equivalent of Solr copyField. Don't replicate via external pipeline.

Performance tuning for queries

  • Cache filter clauses — they're cached by default, faster than must.

  • doc_values: true is default for keyword/numeric/date — required for sort/agg.

  • Use _source filtering to return only needed fields: "_source": ["title", "id"].

  • Avoid term on analyzed text fields — use match instead.

  • Avoid keyword mapping for very high-cardinality string fields if you don't need exact match (slower aggs).

  • Use index: false on fields you store but never search.

  • Profile slow queries with the _search?profile=true flag.

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
Also distributed in
aws/agent-toolkit-for-aws

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Download ZIP

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