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/downloadUnzip 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, itscall_awstool offers a streamlined alternative — but every operation in this skill MUST work via the AWS CLI alone. Data-plane HTTP calls against AOS / AOSS useawscurlfor SigV4-signed requests; this works in both contexts.
Step 0: detect the capability — first thing you do
Pick one of the 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 thecurrent_timetool (returns ISO 8601 in UTC). Read the skill version from this file's frontmatterversion:field. For one-line answers (terse FOCUSED_OPERATIONAL replies, anti-pattern refusals) the header is optional; for any multi-section deliverable it is REQUIRED. Place it immediately after the report title and before the first##heading.No dollar estimates (HARD CONSTRAINT). Never produce
$X/month,~$1,500, or any dollar figure. Route every cost question to https://calculator.aws and stop. If a sub-reference contains dollar figures, treat them as informational context only and do NOT pass them through to the user.No credential leakage (HARD CONSTRAINT). Never include master usernames, KMS key ARNs, VPC endpoint URLs, instance IPs, or account IDs in generated output.
Pick one for every A-vs-B decision. Name a primary recommendation in one line with a one-sentence reason. A "go with B if..." caveat is allowed AFTER the primary; never lead with conditional-only guidance.
Source restatement. The first 2–3 sentences must restate the source (engine + version + scale) when known, or restate the customer's question in concrete terms. The very first text the user sees must NOT be tool narration, meta-commentary, the report title, or simply restating the question verbatim.
No marketing tone. Do NOT use "seamless", "robust", "best-in-class", "production-hardened", "enterprise-grade", "world-class", "cleanly", "elegant". Do NOT stack 3+ vague hedges ("typically", "generally", "usually", "in most cases") in a single recommendation — be specific about when it does and does not apply.
Cross-capability handoff. When a user prompt spans capabilities (e.g., "migrate from Solr AND set up RAG on the new domain"), pick the dominant capability for the response, then close with a one-line handoff: "For <other capability>, see
references/<other-capability>-<entry>.md."
Cross-cutting references (used across multiple capabilities)
These references are not capability-prefixed because they apply across capabilities. Capability entry-point references load them when relevant; SKILL.md never loads them directly.
references/sizing.md— sizing math, instance family details, OR1 trade-offs, watermarks, JVM heap rules.references/vector-knn.md— k-NN engines, memory math, RAG ingestion patterns, ELSER alternatives.references/observability.md— log analytics patterns, ISM, UltraWarm/Cold tiering, Splunk/Datadog migration playbooks.references/security.md— FGAC, encryption, VPC patterns, audit logs, compliance posture.references/personas.md— communication style per persona.references/assessment-gotchas.md— production gotcha catalog (cite by number in Migration specifics or Risks/blockers tables; each gotcha carries aCategory:tag that determines its lane).references/assessment-knowledge-retrieval.md— topic → tool → URL recipe for batched verification.
Assets (assets/): report templates for FULL_ASSESSMENT renderings (Solr-source, ES-source, executive summary).
What this skill does NOT do
Estimate dollar costs. Pricing changes monthly and account-specific (RI, Savings Plan, EDP) discount math is outside this skill's reliable scope. Use https://calculator.aws.
Move data. Use Migration Assistant for Amazon OpenSearch Service (Historical Data Migration for backfill, Live Traffic Migration for live cutover).
Build embedding models. Use Amazon Bedrock or SageMaker.
Replace Splunk SPL or Datadog APM 1:1. Some queries / detectors / dashboards need rewriting.
Tune relevance for a specific catalog. Use OpenSearch Benchmark
big5workload + your own judgment list.
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_skilltool: The skill is not installed on the local filesystem. You MUST fetch each reference or script viaretrieve_skillwith thefileparameter (e.g.file="references/architecture.md"orfile="scripts/deploy.py"), and run the script from the returned content. Do NOTfile_readthese 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/assessment-shape-translation.md
Shape recipe: TRANSLATION_TASK
What this shape is
The user has a working query, request body, or DSL fragment in another search engine (Solr, Elasticsearch ≥ 7.11 syntax that needs OS-side adjustments, raw Lucene syntax, or vendor-specific dialect) and wants the OpenSearch equivalent. The deliverable is drop-in JSON or code that the user can paste into _search, _msearch, a client SDK call, or an OpenSearch Dashboards Dev Tools tab.
This shape is purely a syntactic + semantic mapping exercise. It is NOT a migration assessment, NOT a sizing exercise, and NOT a relevance-tuning engagement.
When to dispatch here
Detect TRANSLATION_TASK when the user prompt contains any of:
"translate this Solr query"
"convert this DSL"
"what's the OpenSearch equivalent of …"
"rewrite this for OpenSearch"
"this is my Solr
q=…&fq=…&qf=…, give me OpenSearch JSON"A pasted Solr URL query string (
/select?q=…&qf=…&pf=…&mm=…&tie=…)A pasted ES query that uses post-fork-only features (e.g.
runtime_mappingsin 7.12+, ES 8.xknntop-level field) and the user is targeting AOS managedA pasted Lucene
q=string with field-prefix syntax (title:headphones AND brand:sony)
Do NOT dispatch here when:
The user pasted
schema.xmlor an ES mapping → that's SCHEMA_CONVERSION.The user wants migration tooling guidance ("how do I move my Solr docs to OS") → that's FULL_ASSESSMENT or FOCUSED_OPERATIONAL.
The user asks "how do I write a query that does X" with no source DSL → that's a search-recipe lookup; serve from
references/search-recipes.mddirectly.
Required output template
Produce these sections in order, nothing more:
1. Source restatement (1 sentence)
"Translating Solr 8.11 eDisMax
q=wireless headphones&qf=title^3 description&pf=title^5&mm=2<-25%&q.op=AND&tie=0.3to OpenSearch 2.x_search."
State source engine + version (if known) + the specific query type (eDisMax, dismax, standard, function query, JSON Facet API, …) + target endpoint.
2. Drop-in JSON / code (the deliverable)
A single fenced code block, valid JSON, ready to paste into Dev Tools. Preserve field names exactly. Include query, from/size, sort, aggs, highlight blocks as the source request had them.
3. Translation fidelity table
For every non-trivial Solr/ES parameter in the source, one row showing source param → OpenSearch param → fidelity (verbatim / mapped / approximation). This is the heart of the shape — every syntactic element must be either preserved or explicitly mapped, with no silent drops.
4. Approximation caveats (inline, only if any rows in the table are "approximation")
A short bullet list of behavior drift the user must be aware of. Examples:
pf(phrase boost): modeled as ashouldclause withmulti_match type: phrase. Scoring shape differs — Solr'spfboosts the whole phrase score additively; OpenSearch'sshouldadds a separately-scored phrase match. Re-tune boost values against your judgment list.tie_breakerdefault: OpenSearchmulti_match best_fieldsdefaultstie_breaker: 0.0(winner-takes-all); Solr eDisMax defaultstie=0.0as well, but if the source omittedtie, set it explicitly to avoid surprises if you later upgrade.q.op=AND: OpenSearchquery_stringdefaults to OR. Setdefault_operator: ANDexplicitly or results will diverge.
5. Verification snippet (optional, 1–3 lines)
If the translation is non-trivial, give the user a 1-line _validate/query?explain=true or a 2-doc sanity check they can run to confirm the rewrite parses and scores reasonably.
NOT REQUIRED — explicitly OMIT
Do NOT produce these sections in TRANSLATION_TASK:
Timeline & Resourcing — removed from the suite. Do NOT estimate engineer-weeks, sprint count, or calendar duration anywhere.
Executive Summary — translation is tactical, not a deliverable that needs an exec frame.
Source / Target / Migration Path / Risks — the four big assessment sections; not applicable here.
Sizing / Readiness scorecard — translation has no infra footprint.
Citations section — only include if you make ≥3 version-volatile claims (e.g. "this only works in OS 2.13+"). For a normal Solr→OS query rewrite, citations are noise.
Migration tooling discussion — do NOT pivot to "and to move your data, use Migration Assistant for Amazon OpenSearch Service…". Stay in the lane.
Dollar costs — universal hard constraint; never produce a dollar figure.
Persona-aware framing — the asker self-selected by pasting DSL; treat them as a search engineer.
Solr → OpenSearch query translation reference table
This is the canonical lookup. Use it; do NOT re-derive per request.
| Solr (or ES 7.x dialect) | OpenSearch | Fidelity | Notes |
|---|---|---|---|
q=headphones | {"multi_match": {"query": "headphones", "fields": ["title", "description"]}} | mapped | Solr searches df (default field); OS has no _all — name fields explicitly. |
q=title:headphones | {"match": {"title": "headphones"}} | verbatim | Field-scoped match. |
q.op=AND | "default_operator": "AND" (on query_string) or "operator": "AND" (on match / multi_match) | verbatim | OpenSearch defaults to OR. #1 cause of result divergence. Always set explicitly. |
defType=edismax | multi_match type: best_fields | mapped | Closest semantic equivalent; not byte-identical scoring. |
qf=title^3 description^1 tags^2 | "fields": ["title^3", "description^1", "tags^2"] | verbatim | Boosts pass through unchanged. |
pf=title^5 (phrase boost) | should: [{"multi_match": {"query": "<q>", "type": "phrase", "fields": ["title^5"]}}] | approximation | Scoring shape differs — see caveats. |
pf2=title^3 / pf3=title^2 | Two should clauses with match_phrase and slop adjustment | approximation | Solr's bigram/trigram phrase boost has no exact OS equivalent. |
tie=0.3 | "tie_breaker": 0.3 (on multi_match best_fields) | verbatim | Same semantics. |
mm=2<-25% | "minimum_should_match": "2<-25%" | verbatim | Syntax passes UNCHANGED — same parser. |
mm=100% | "minimum_should_match": "100%" | verbatim | All clauses must match. |
fq=in_stock:true | bool.filter: [{"term": {"in_stock": true}}] | verbatim | Filter context — no scoring, cacheable. |
bq=category:electronics^2 (boost query) | should: [{"term": {"category": {"value": "electronics", "boost": 2}}}] | verbatim | Additive scoring boost. |
bf=recip(ms(NOW,timestamp),3.16e-11,1,1) (boost function) | function_score with gauss or exp decay on timestamp | mapped | Solr recip is a hyperbolic decay; OS gauss/exp give equivalent shape — re-tune scale. |
sort=score desc, price asc | "sort": [{"_score": "desc"}, {"price": "asc"}] | verbatim | score → _score. |
start=20&rows=20 | "from": 20, "size": 20 | verbatim | |
fl=title,price,_score | "_source": ["title", "price"] (_score always returned) | verbatim | |
hl=true&hl.fl=title | "highlight": {"fields": {"title": {}}} | verbatim | |
facet=true&facet.field=category | "aggs": {"by_category": {"terms": {"field": "category.keyword"}}} | mapped | Use .keyword sub-field for terms agg. |
facet.range=price&facet.range.start=0&facet.range.end=100&facet.range.gap=20 | "aggs": {"price_ranges": {"range": {"field": "price", "ranges": [{"to":20},{"from":20,"to":40},…]}}} | mapped | OS uses explicit range buckets. |
mlt=true | more_like_this query | mapped | Same semantics, different request shape. |
wt=json | (default) Accept: application/json | verbatim | OS speaks JSON natively. |
group=true&group.field=brand | "collapse": {"field": "brand.keyword"} | mapped | OS collapse ≈ Solr field collapsing for top-1 per group. |
cursorMark=* | search_after + tiebreaker sort | mapped | OS deep pagination via PIT + search_after. |
Worked exemplar
User: Translate this Solr query:
q=wireless headphones&defType=edismax&qf=title^3 description^1 tags^2&pf=title^5&mm=2<-25%&q.op=AND&tie=0.3&fq=in_stock:true&start=0&rows=20&sort=score desc, price asc&hl=true&hl.fl=titleAssistant response (this is the shape):
Translating Solr 8.x eDisMax q=wireless headphones (qf/pf/mm/tie/q.op/fq/sort/highlight) to OpenSearch 2.x _search.
GET products/_search
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "wireless headphones",
"type": "best_fields",
"fields": ["title^3", "description^1", "tags^2"],
"operator": "AND",
"minimum_should_match": "2<-25%",
"tie_breaker": 0.3
}
}
],
"should": [
{
"multi_match": {
"query": "wireless headphones",
"type": "phrase",
"fields": ["title^5"]
}
}
],
"filter": [
{"term": {"in_stock": true}}
]
}
},
"from": 0,
"size": 20,
"sort": [{"_score": "desc"}, {"price": "asc"}],
"highlight": {"fields": {"title": {}}}
}| Solr param | OpenSearch | Fidelity |
|---|---|---|
defType=edismax | multi_match type: best_fields | mapped |
qf=title^3 description^1 tags^2 | fields: ["title^3","description^1","tags^2"] | verbatim |
pf=title^5 | should + multi_match type: phrase | approximation |
mm=2<-25% | minimum_should_match: "2<-25%" | verbatim |
q.op=AND | operator: "AND" | verbatim |
tie=0.3 | tie_breaker: 0.3 | verbatim |
fq=in_stock:true | filter term clause | verbatim |
sort=score desc, price asc | sort: [{"_score":"desc"},{"price":"asc"}] | verbatim |
hl=true&hl.fl=title | highlight.fields.title | verbatim |
Approximation caveats:
pfphrase boost: modeled as a separateshouldclause; Solr eDisMax adds thepfscore to the eDisMax sum, OpenSearch adds theshouldscore to the bool sum. Top-N order should be similar but not identical — re-tunepfboost (using the example value^5) against your judgment list.q.op=AND: set asoperator: "AND"onmulti_match(cleaner thanquery_stringfor this case). OpenSearch defaults to OR — without this, recall would explode.
To sanity-check parsing: POST products/_validate/query?explain=true with the same body — confirms the BoolQuery / DisjunctionMaxQuery structure matches expectation.
Pre-emit checklist (TRANSLATION_TASK-specific)
Before sending the response, tick every box:
[ ] First sentence is the source restatement (engine + version + query type + target endpoint), not tool narration.
[ ] Output contains a single, valid, copy-pasteable JSON block for
_search(or the right endpoint).[ ] Every parameter from the source request is either present in the OS JSON or explicitly mapped in the fidelity table — no silent drops.
[ ]
q.op/default_operatoris handled explicitly: if the source had AND (Solrq.op=ANDOR an explicitAND/&&boolean operator inside a Lucenequery_stringquery), it MUST appear in the JSON itself —operator: "AND"onmatch/multi_match, ordefault_operator: "AND"onquery_string. Discussing it only in prose or in the approximation caveats does NOT satisfy this rule (the customer is told to drop the JSON in directly, so the JSON has to be correct on its own). Apply this to every query in a multi-query translation, not only the first.[ ]
mm/minimum_should_matchpreserved verbatim (same parser; do not "simplify"2<-25%).[ ] eDisMax
qfboosts preserved verbatim inmulti_match.fields.[ ] Any
pf/pf2/pf3is in a separateshouldclause AND flagged in caveats as approximation.[ ]
tie_breakeris set explicitly when the source hadtie(do not rely on defaults).[ ] Field names that need
.keywordsub-field forterm/terms agg/sortare using it.[ ] NO
Timeline & Resourcingsection. NO Executive Summary. NO Sizing. NO migration tooling pivot.[ ] NO dollar figures anywhere.
[ ] No marketing tone ("seamless", "robust", "best-in-class", "elegant", "cleanly").
[ ] Citations section omitted unless ≥3 version-volatile claims were made.
[ ] If approximation rows exist, the caveats bullet list is present — never leave approximation unflagged.
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
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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
- 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