Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/elastic/cursor-plugins/elasticsearch-onboardingnpx skills add elastic/cursor-plugins --skill elasticsearch-onboardinggit clone --depth 1 https://github.com/elastic/cursor-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/elastic/cursor-plugins/elasticsearch-onboarding)<a href="https://agentmods.dev/skills/elastic/cursor-plugins/elasticsearch-onboarding"><img src="https://agentmods.dev/badge/skills/elastic/cursor-plugins/elasticsearch-onboarding.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00075 | $0.00568 |
| Opus 5 | $0.00037 | $0.00284 |
| Sonnet 5 | $0.00015 | $0.00114 |
| Haiku 4.5 | $0.00007 | $0.00057 |
Grade A, and why
elasticsearch-onboarding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to elasticsearch-onboarding — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
- 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.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/catalog-ecommerce/ecommerce.md 16 KB
- references/code-generation/code-generation.md 6.5 KB
- references/elasticsearch-onboarding-playbook.md 23 KB
- references/keyword-search/keyword-search.md 9.9 KB
- references/mcp-setup/mcp-setup.md 6.6 KB
- references/rag-chatbot/rag-chatbot.md 16 KB
- references/search-ui/search-ui.md 21 KB
- references/use-case-library/use-case-library.md 10 KB
- references/vector-hybrid-search/vector-hybrid-search.md 21 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 55 lines · 75 tokens per session scan A 56980d007ce9
elasticsearch-onboarding is a skill published in the GitHub repository elastic/cursor-plugins (31 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 568 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to elasticsearch-onboarding, differing in 0 lines, and is treated as a copy.
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