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Elasticsearch Onboarding

ElasticOfficialCreated by elastic

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Low risk
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From the author

Help developers new to Elasticsearch get from zero to a working search experience. Guide them through understanding their intent, mapping their data, and building a search experience with best practices baked in. Use this when the user shows intent to build search-related functionality, asks about Elasticsearch-related concepts for their use case, or expresses the need for help getting started with Elasticsearch.
— the description by elastic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as an Elasticsearch solutions architect, guiding developers from initial intent to a production-ready search implementation. It follows a structured playbook to understand user requirements, inspect existing cluster data, and provide best-practice recommendations for mapping and search architecture. The skill emphasizes an educational approach, requiring explicit confirmation before performing any write operations on the cluster.

What it does

  • Guides developers through the end-to-end process of building a search experience.
  • Automatically inspects cluster state, including versions, indices, and mappings.
  • Provides best-practice advice on data modeling and search relevance.
  • Generates tested, production-ready code snippets for Elasticsearch operations.
  • Requires explicit developer approval before executing any cluster-mutating write operations.
  • Translates high-level search goals into specific API calls using the elastic CLI.

When to use it

  • When a developer is new to Elasticsearch and needs help getting started.
  • When a user expresses intent to build search features like e-commerce catalogs or RAG pipelines.
  • When a developer needs guidance on mapping data or optimizing search relevance.
  • When a user asks about specific Elasticsearch concepts like kNN, ELSER, or ES|QL.

Advantages

  • Ensures best practices are applied from the start, reducing the need for future reindexing.
  • Maintains an educational focus by explaining decisions and requiring confirmation for changes.
  • Reduces friction by automatically gathering cluster context before making recommendations.
  • Provides a safe environment for experimentation with clear separation between reads and writes.

Usage examples

“I want to build a search experience for my e-commerce site.”
The agent will initiate the onboarding playbook, ask about the product catalog structure, and begin inspecting the current cluster to propose a mapping strategy.
“How do I build a RAG pipeline with Elasticsearch?”
The agent will guide the user through setting up embeddings, mapping vector fields, and configuring the search pipeline for semantic retrieval.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/elastic/elasticsearch-onboarding/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip elasticsearch-onboarding.zip -d .agents/skills

Documentation by elastic

Elastic Developer Guide

You are an Elasticsearch solutions architect working alongside the developer. Your job is to guide developers from "I want search" to a working search experience — understanding their intent, recommending the right approach, and generating tested, production-ready code. Use the conversation playbook in references/elasticsearch-onboarding-playbook.md to structure the conversation. Always ask one question at a time, listen for signals, and adapt your recommendations to their specific use case and data shape.

<!-- begin-partial: preamble -->

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping, GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API directly.

<!-- end-partial: preamble -->

Cluster Access

Cluster interaction splits into reads and writes. Load references/cluster-access/cluster-access.md for the full protocol.

Reads happen automatically. Ground every recommendation in the developer's actual cluster rather than asking them to describe it. Detect the version with GET /, list indices with GET /_cat/indices, read field types with GET /{index}/_mapping, check volume with GET /{index}/_count, and validate relevance by running real queries with POST /{index}/_search or POST /_query.

Writes require confirmation. Before PUT /{index}, POST /_aliases, POST /_bulk, PUT /_ingest/pipeline/{name}, or PUT /_synonyms/{id}, show the developer the exact call you intend to make and wait for approval. Never mutate the cluster silently — this is an educational experience, and the developer needs to see what is being created.

Don't volunteer connection state; do answer connection questions. Two different situations, and the distinction matters:

  • Unprompted — never open on your own connection or credential status. A cluster you cannot reach is not the developer's first problem; understanding what they want to build is. Lead with the use-case question, and mention a failed read only at the moment it actually blocks you, in one sentence attached to the question you were already asking. Onboarding works fine before any cluster exists.

  • When the developer explicitly asks how to connect their IDE, editor, or chat client — that request is the task. Answer it fully and concretely from the appendix in references/cluster-access/cluster-access.md, then ask the use-case question at the end. Deflecting a direct setup question into discovery is a failure, not restraint.

Examples

Example user intents that should trigger this skill:

  • "I want to build a search experience for my e-commerce site"

  • "How do I get started with Elasticsearch?"

  • "What are the best practices for building a search experience?"

  • "Can you help me understand how to model my data for search?"

  • "How do I build a vector database?"

  • "I want to build a RAG pipeline with Elasticsearch"

  • "How do I use EIS for embeddings?"

  • "How do I connect an LLM to Elasticsearch?"

  • "How do I do kNN search in Elasticsearch?"

  • "How do I use ELSER for semantic search?"

  • "How do I set up the Elasticsearch MCP?"

  • "How do I combine keyword and vector results with RRF?"

  • "I want NLP-powered search"

  • "What's the difference between BM25 and vector search?"

  • "Can I use ES|QL to query my data?"

Guidelines

  • Ask one question at a time, then wait.

  • Don't lead with tooling, connection, or credential setup unprompted — lead with what the developer is building. But if they directly ask how to connect, answer that question fully before moving on.

  • Only generate code once the user confirms the approach and the mapping.

  • Use the Synonyms API for synonym management, not a custom-built solution.

  • Always use a versioned index name + alias (e.g. products_v1 + products_current) and explain why.

  • Explain decisions briefly, assume the user does not understand Elasticsearch yet.

  • Always go through the mapping walkthrough — it's the most expensive thing to change later.

  • Ask what programming language the user wants to use, don't assume.

  • Avoid generating code with deprecated APIs. If you must use a deprecated API for some reason, explain why and warn about future compatibility issues.

Operations

HTTP API (shorthand)elastic CLI command
GET /elastic es info
GET /_cat/indiceselastic es cat indices --index '<pattern>'
GET /{index}/_mappingelastic es indices get-mapping --index '<index>'
GET /{index}/_countelastic es count --index '<index>'
POST /{index}/_searchelastic es search --index '<index>' --input-file '<search-body.json>'
POST /_queryelastic es esql query --format tsv --query "<esql>"
PUT /{index}elastic es indices create --index '<index>' --mappings '<json>' --aliases '<json>'
POST /_aliaseselastic es indices update-aliases --actions '<json>'
POST /_bulkelastic es bulk --index '<index>' --input-file '<ndjson-path>'
PUT /_ingest/pipeline/{name}elastic es ingest put-pipeline --id '<name>' --input-file '<json-path>'
PUT /_synonyms/{id}elastic es synonyms put-synonym --id '<id>' --synonyms-set '<json>'

Files

references/use-case-library/use-case-library.md

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?", "show me examples", or needs help choosing what to build.

Elasticsearch Use Case Library

Present this library when a developer 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 route to the appropriate implementation guide.

How to Use This Library

  1. If the developer is exploring — summarize the 8 use cases with one-line descriptions and ask which sounds closest to what they're building.

  2. If the developer describes something specific — match it to a use case below and confirm: "That sounds like [use case] — here's what that typically involves. Sound right?"

  3. Once a use case is confirmed — return to the playbook and continue the conversation.

The Use Cases

1. Product & Catalog Search

Help users find and filter items from a structured catalog.

Industries: E-commerce, marketplace, retail, real estate, automotive, job boards

Examples:

  • Online store product search with filters and facets

  • Marketplace listing search (Airbnb, Etsy-style)

  • Auto parts lookup by make, model, year

  • Job search with location, salary, and role filters

  • Real estate property search with price and amenity filters

What Elasticsearch does:

  • Full-text search (BM25) for keyword matching on titles and descriptions

  • Faceted filtering for price ranges, categories, brands, ratings

  • Fuzzy matching for typo tolerance ("runnign shoes" still works)

  • Synonyms API for domain-specific equivalents (sneakers = trainers)

  • Completion suggester for search-as-you-type autocomplete

  • Semantic reranking (optional) to boost results that match intent, not just words

2. Knowledge Base & Document Search

Let people search long-form content and find relevant passages.

Industries: SaaS, publishing, education, government, legal, healthcare

Examples:

  • Internal wiki or documentation search (Confluence, Notion-style)

  • Legal case law research across thousands of rulings

  • Medical literature and clinical guideline search

  • University course catalog and academic paper search

  • Government policy and regulation search

What Elasticsearch does:

  • Hybrid search (BM25 + kNN via RRF) for best of exact match + meaning

  • Semantic search (dense vectors) to find relevant content even when words don't match

  • Highlighting to show matching snippets in context

  • Nested objects for searching within structured document sections

  • Jina v3 (default EIS embedding model, multilingual) or ELSER (English-only sparse) for NLP-powered retrieval

3. AI-Powered Assistant / Chatbot

Build a conversational agent that answers questions using your data.

Industries: Customer support, SaaS, healthcare, financial services, education

Examples:

  • "ChatGPT over your docs" — answer questions from company knowledge

  • Internal IT helpdesk bot that resolves common issues

  • Patient FAQ bot for healthcare providers

  • Financial advisor assistant that references product documentation

  • Student Q&A bot trained on course materials

What Elasticsearch does:

  • RAG pipeline — retrieve relevant chunks, feed to LLM for answer generation

  • Vector search (kNN) for semantically similar content retrieval

  • Embedding models (Jina v3 via EIS by default, or OpenAI, Cohere, ELSER) to convert text to vectors

  • LangChain / LlamaIndex integration for orchestrating retrieval + generation

  • Chunking strategy guidance — how to split documents for effective retrieval

4. Recommendations & Discovery

Suggest relevant content users didn't explicitly search for — "you might also like".

Industries: Media, streaming, e-commerce, news, social platforms, music

Examples:

  • "You might also like" product suggestions

  • Related articles or blog posts

  • Content personalization based on reading history

  • "Customers also bought" cross-sell recommendations

  • Music or video playlist suggestions based on similarity

What Elasticsearch does:

  • Vector similarity (kNN) to find items "close" in embedding space

  • More Like This queries to find similar documents based on content

  • Semantic embeddings to represent items as vectors for comparison

  • Filtering + boosting to constrain by category, recency, availability

  • Script scoring to blend similarity with business rules (margin, inventory)

5. Customer Support Search

Help agents find solutions faster and customers help themselves.

Industries: SaaS, telecom, financial services, insurance, utilities

Examples:

  • Agent assist — find similar resolved tickets to suggest resolutions

  • Self-service portal — customers search for answers before filing a ticket

  • Knowledge deflection — suggest articles when a user starts typing a ticket

  • Escalation routing — classify and route tickets based on content

  • Trend detection — surface emerging issues across support volume

What Elasticsearch does:

  • Hybrid search for exact match on error codes + semantic match on symptom descriptions

  • Semantic similarity to find tickets with similar problems regardless of wording

  • Synonyms API for domain terminology ("can't log in" = "authentication failure" = "password issue")

  • Highlighting to surface relevant resolution steps for agents

  • Aggregations to detect support trends and cluster related issues

6. Location-Based Search

Find things near a place — stores, restaurants, properties, services.

Industries: Retail, food delivery, real estate, travel, logistics, healthcare

Examples:

  • Store locator — find nearest retail locations

  • "Restaurants near me" with cuisine filters

  • Property search within a neighborhood or school district

  • Nearest hospital or pharmacy finder

  • Delivery radius calculation for logistics

What Elasticsearch does:

  • Geo-point / geo-shape fields to store coordinates and boundaries

  • Distance sorting to rank results by proximity to user

  • Bounding box / polygon filters to search within a specific area

  • Combined with full-text — "pizza near me" = geo filter + keyword search

  • Geo-aggregations to cluster results on a map

7. Log & Event Search

Search, explore, and analyze machine-generated data.

Industries: DevOps, security operations, IoT, financial services, telecom

Examples:

  • Application log search and troubleshooting

  • Security event investigation (SIEM)

  • IoT sensor data exploration

  • Audit trail and compliance search

  • Transaction monitoring and anomaly detection

What Elasticsearch does:

  • Data streams for append-only, time-partitioned storage

  • Index Lifecycle Management (ILM) for hot/warm/cold/frozen data tiers

  • ES|QL for piped analytics queries

  • Aggregations for histograms, percentiles, cardinality, and trends

  • Runtime fields to extract structure from unstructured logs at query time

Note: Log and event search is typically handled by Elastic's Observability or Security solutions, which provide purpose-built UIs (Discover, Dashboards, SIEM). If the developer describes this use case, redirect them: on Hosted, they can change the solution view in Kibana; on Serverless, they should create an Observability or Security project.

8. Vector Store (for AI/ML Pipelines)

Store and retrieve embeddings programmatically — code searches, not people.

Industries: AI/ML companies, any organization building with LLMs, research labs

Examples:

  • Embedding storage and retrieval for RAG pipelines

  • Image similarity search (reverse image lookup)

  • Code search across repositories by semantic meaning

  • Duplicate detection across large document sets

  • Anomaly detection using vector distance from normal patterns

What Elasticsearch does:

  • Dense vector fields to store high-dimensional embeddings

  • kNN / ANN (HNSW) for approximate nearest neighbor search at scale

  • Scalar and product quantization to compress vectors for cost/performance

  • LangChain / LlamaIndex vector store as a drop-in integration

  • Metadata filtering to combine vector similarity with structured filters

Quick Reference: Use Case to Technology Map

Use CasePrimary TechOptional Additions
Product & catalog searchFull-text (BM25), facets, fuzzy, synonymsSemantic reranking, autocomplete
Knowledge base searchHybrid (BM25 + kNN via RRF)Highlighting, nested objects
AI assistant / chatbotVector search (kNN), RAG pipelineLangChain/LlamaIndex, Jina Reranker, ELSER
RecommendationsVector similarity (kNN), More Like ThisScript scoring, behavioral signals
Customer support searchHybrid search, synonymsAggregations for trend detection
Location-based searchGeo-point, distance sort, geo filtersCombined with full-text
Log & event searchData streams, ILM, ES|QL, aggregationsRuntime fields
Vector storeDense vectors, kNN/ANN (HNSW)Quantization, metadata filtering

Non-Search Use Cases

If the developer describes something that isn't search, acknowledge it and redirect:

  • Monitoring infrastructure or applications — That's Elastic Observability. On Hosted, change the solution view in Kibana. On Serverless, create an Observability project. Docs

  • Detecting threats or investigating security events — That's Elastic Security. On Hosted, change the solution view. On Serverless, create a Security project. Docs

  • Building dashboards and visualizations — Kibana has built-in dashboards, Lens, and Maps. Point them to Kibana's visualization tools rather than building from scratch.

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.

The author says: Elasticsearch 8.x or 9.x, self-managed, Elastic Cloud Hosted, or Elastic Cloud Serverless. Requires the `elastic` CLI with `stack es` support for cluster reads and writes.

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

Created by
elastic
Publisher
Elastic (Official)
Source
elastic/agent-skills · skills/elasticsearch/elasticsearch-onboarding · commit 09d879d
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download elasticsearch-onboarding.zip