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Elasticsearch use case library — the full map of what you can build, with industry examples and technologies. Use when a developer asks "what can Elastic do?", "what can I build?", "what use cases does Elasticsearch support?", or needs help choosing what to build.
Based on SOC occupation classification
| name | use-case-library |
| description | Elasticsearch use case library — the full map of what you can build, with industry examples and technologies. Use when a developer asks "what can Elastic do?", "what can I build?", "what use cases does Elasticsearch support?", or needs help choosing what to build. |
Present this library when a user asks what they can build with Elasticsearch, wants to explore use cases, or needs help figuring out which category their project falls into. Walk through the relevant use cases conversationally — don't dump the entire list. Ask what resonates, then continue the conversation.
This skill helps users explore what they can build. It is not the main conversation driver.
Once the user picks a use case or is ready to start building, re-read /elasticsearch-onboarding to enter the structured onboarding playbook (Steps 1–7: intent → data → mapping → build → test → iterate). That playbook controls sequencing, the one-question-at-a-time rule, and the Dev Tools API-snippet workflow. If /elasticsearch-onboarding has not been loaded yet in this conversation, load it now — it is the primary conversation flow for all Elasticsearch search onboarding.
Help users find and filter items from a structured catalog.
Industries: E-commerce, marketplace, retail, real estate, automotive, job boards
Examples:
What Elasticsearch does:
In Kibana: Create the index and mapping via Dev Tools, set up synonyms, ingest sample data with POST _bulk, and test queries — all before writing any application code.
Let people search long-form content and find relevant passages.
Industries: SaaS, publishing, education, government, legal, healthcare
Examples:
What Elasticsearch does:
semantic_text for meaning-based retrievalIn Kibana: Set up the index with semantic_text fields via Dev Tools, configure inference endpoints, and test hybrid queries. The retrieval backend is fully configured before any frontend work.
Build a conversational agent that answers questions using your data.
Industries: Customer support, SaaS, healthcare, financial services, education
Examples:
What Elasticsearch does:
In Kibana: Set up the vector index, configure the inference endpoint, ingest and chunk documents via Dev Tools. The retrieval layer is ready to connect to an LLM from your application code in the IDE.
Suggest relevant content users didn't explicitly search for.
Industries: Media, streaming, e-commerce, news, social platforms
Examples:
What Elasticsearch does:
In Kibana: Create the vector index, ingest item embeddings, and test similarity queries in Dev Tools. Integrate the query into your application afterward.
Help agents find solutions faster and customers help themselves.
Industries: SaaS, telecom, financial services, insurance, utilities
Examples:
What Elasticsearch does:
In Kibana: Set up the index with hybrid fields, create synonym sets, and test queries in Dev Tools. The search backend is production-ready before building the support UI.
Find things near a place — stores, restaurants, properties, services.
Industries: Retail, food delivery, real estate, travel, logistics
Examples:
What Elasticsearch does:
geo_point / geo_shape fields for coordinates and boundariesIn Kibana: Create the index with geo fields, bulk-index location data via Dev Tools, and test distance queries. Combine with full-text search in the same index.
Search, explore, and analyze machine-generated data.
Industries: DevOps, security operations, IoT, financial services
Examples:
What Elasticsearch does:
Note: Log and event search is typically handled by Elastic's Observability or Security solutions with purpose-built UIs. In Kibana: switch solution view under Management → Spaces (Hosted) or create an Observability/Security project (Serverless).
Store and retrieve embeddings programmatically — code searches, not people.
Industries: AI/ML companies, any organization building with LLMs
Examples:
What Elasticsearch does:
In Kibana: Create the vector index, configure quantization, and test kNN queries in Dev Tools. Connect your AI pipeline from your IDE using the Elasticsearch client library.
| Use Case | Primary Tech | Set Up in Kibana |
|---|---|---|
| Product & catalog search | Full-text (BM25), facets, synonyms | Index + mapping + synonyms via Dev Tools |
| Knowledge base search | Hybrid (BM25 + kNN via RRF) | Index with semantic_text + inference endpoint |
| AI assistant / chatbot | Vector search (kNN), RAG | Vector index + chunking pipeline via Dev Tools |
| Recommendations | Vector similarity, More Like This | Vector index + similarity queries |
| Customer support search | Hybrid search, synonyms | Index + synonym sets + hybrid queries |
| Location-based search | geo_point, distance sort | Index with geo fields + distance queries |
| Log & event search | Data streams, ILM, ES|QL | Use Observability/Security solution view |
| Vector database | Dense vectors, kNN/ANN | Vector index + quantization config |
If the user describes something that isn't search, redirect within Kibana:
Register and implement custom workflow steps from an external Kibana plugin using `@kbn/workflows-extensions`. Use when adding or modifying a step type with `registerStepDefinition`, designing input/output/config Zod schemas, implementing `createServerStepDefinition` / `createPublicStepDefinition`, choosing `StepCategory`, building `editorHandlers` (selection / dynamicSchema), wiring `callKibanaApi` / `onCancel`, deciding sync vs async loader registration, updating `APPROVED_STEP_DEFINITIONS`, or reviewing PRs that touch any of these.
Register and implement custom workflow triggers from an external Kibana plugin using `@kbn/workflows-extensions`. Use when adding or modifying an event-driven trigger with `registerTriggerDefinition`, designing `eventSchema` Zod schemas, writing `documentation` and KQL `snippets`, wiring `emitEvent` via request context or `getClient`, choosing sync vs async public loader registration, updating `APPROVED_TRIGGER_DEFINITIONS`, or reviewing PRs that touch any of these. Always ask for the user's plugin id first to locate the correct plugin and file paths.
Register and roll out managed workflows from a Kibana plugin using `@kbn/workflows-extensions` and `@kbn/workflows/managed`. Use when adding or modifying a code-owned workflow definition, `registerManagedWorkflowOwner`, `initManagedWorkflowsClient`, `install` / `uninstall` / `ready`, choosing `lifecycle` / `versionStrategy` / `enablement`, authoring `yaml` vs `yamlTemplate`, space-scoped vs global installs, `getWorkflowStatus`, or `execute`, or reviewing PRs that touch managed workflow definitions or rollout. Always ask for the user's plugin id first to locate the correct plugin and definition file paths.
Implement and quality-check OpenTelemetry metric instrumentation in Kibana code that uses `@kbn/metrics`. Use whenever the user wants to add, change, or review OTel metrics — including any call to `metrics.getMeter`, `meter.createCounter`/`createUpDownCounter`/`createGauge`/`createHistogram`/`createObservable*`/`addBatchObservableCallback`, edits to `kibana.yml` `telemetry.metrics` config, or questions like "is this metric well-designed?", "what should I name this counter?", or "which instrument type is right here?". Trigger this skill even when the user does not say "OTel" or "OpenTelemetry" but is clearly adding observability to Kibana server code and already knows what they want to measure.
Primary guided playbook for Elasticsearch search in Kibana Agent Builder: intent → data → mapping → Dev Tools API snippets (SENSE), with one question at a time. Load this skill whenever the user wants to learn Elasticsearch search, get started, begin building, take first steps, onboard, follow a walkthrough or tutorial, go from zero to a working query, or get structured help setting up indices and search — including casual openers like hi, help, getting started, new to Elasticsearch, how do I build search, or I want to try search. Use when they need end-to-end onboarding, not a single narrow API answer. If they only ask what they can build with Elastic (exploration without the full playbook), prefer invoking /use-case-library first; you can still load this skill afterward for the guided build.
Topic-driven, hands-on Elasticsearch tutorial flow that runs in Kibana Dev Console. Use whenever the user says "walk me through", "give me a tutorial for", "teach me", "show me how X works", "tutorial on", or similar topical learning intent — and they are NOT asking you to build their real, specific use case. Topics are open-ended: any Elasticsearch / Kibana search concept the user names (e.g. mappings, analyzers, bool queries, semantic_text, kNN, RRF, aggregations, ingest pipelines, reranking, data streams, ES|QL). Tutorials use sample data on isolated resources, present every step as a SENSE snippet to run in Dev Tools, and end with cleanup plus pointers to docs and the onboarding / pattern skills.