elasticsearch

elasticsearch is a skill for Claude Code, Codex from withoneai/one-agent-plugin. It costs 108 tokens per session (5,830 once invoked), scanned A, a copy of 2-chat, MIT.

A distributed search and analytics engine for finding text and structured data across large datasets. It is commonly used for logs, monitoring, discovery, and real-time analysis.

In plain words
What is it for?
It is for full-text search, structured queries, log analysis, monitoring data, discovery features, and real-time analysis across distributed data.
Why use it?
It helps applications search and analyze large volumes of information quickly instead of scanning every record one by one.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for full-text search, structured queries, log analysis, monitoring data, discovery features, and real-time analysis across distributed data.

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Install with agentmods
npx agentmods add skills/withoneai/one-agent-plugin/elasticsearch
Install

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.

Any agent
npx skills add withoneai/one-agent-plugin --skill elasticsearch
Clone the repo
git clone --depth 1 https://github.com/withoneai/one-agent-plugin

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for elasticsearch

README.md
[![agentmods](https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/elasticsearch/github.svg)](https://agentmods.dev/skills/withoneai/one-agent-plugin/elasticsearch)
Your own site
<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/elasticsearch"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/elasticsearch/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for elasticsearch

Your own site · 80×15
<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/elasticsearch"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/elasticsearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,830 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 80% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00108 $0.05830
Opus 5 $0.00054 $0.02915
Sonnet 5 $0.00022 $0.01166
Haiku 4.5 $0.00011 $0.00583

Measured 6d ago against content hash 79476027fc0b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

elasticsearch 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 6d 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.

Origin

This is a copy

80% identical to 2-chat — 324 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.

platforms/one-elasticsearch/skills/elasticsearch/SKILL.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Elasticsearch through One

A distributed, RESTful search and analytics engine that enables applications to perform fast full-text search, structured querying, and real-time data analysis across large volumes of data for logging, monitoring, and discovery use cases.

One exposes Elasticsearch through four MCP tools. The table below carries real action ids from One's knowledge base, so for a common operation you can skip search and go straight to reading the action's parameters.

How to run an action

  1. Find the action in the table below, or call search_one_platform_actions with platform elasticsearch if it is not listed.
  2. Call get_one_action_knowledge with the action id. Do this every time, including for actions in this table. The table gives you the id, not the parameters.
  3. Call execute_one_action with parameters copied from that knowledge.

Never guess a parameter name, a body field, or an enum value. The knowledge has the real schema, and a guessed field is either a 400 or a silent write of the wrong thing.

Before you start

Call list_one_integrations once and confirm Elasticsearch is connected. If it is missing, the user has not connected it: say so and point them at https://app.withone.ai rather than reaching for raw HTTP.

Each connection carries an access field. If it reports {"policy": "methods", "methods": ["GET"]} the agent is read-only here, so plan a read-only answer instead of attempting a write that will be refused.

Before a write

Creates, updates, deletes and sends land on a real Elasticsearch account and cannot be recalled. State the action and the specific target in one line before the first write in a task, and let the user stop you. Reads need no confirmation.

Actions

InferenceEndpoints

Action Method Path Action id
Get an Inference Endpoint (by Task Type and Inference ID) GET /_inference/{{taskType}}/{{inferenceId}} conn_mod_def::GKBWys21fw0::hKiHVH7yRMy708lYdJ9MWw
Create a Cohere Inference Endpoint PUT /_inference/{{taskType}}/{{cohereInferenceId}} conn_mod_def::GKBWvAJ03gI::_cpnQY7rS8CBWv9O13SsYw
Create a Contextual AI Inference Endpoint PUT /_inference/{{taskType}}/{{contextualaiInferenceId}} conn_mod_def::GKBWxUh7_og::oXaheHK2T0KTUvgxV03eYA
Create a DeepSeek Inference Endpoint PUT /_inference/{{taskType}}/{{deepseekInferenceId}} conn_mod_def::GKBWvPc47wY::G97evLY-TmOUVAGCsm43EQ
Create a Fireworks AI Inference Endpoint PUT /_inference/{{taskType}}/{{fireworksaiInferenceId}} conn_mod_def::GKBWvXfWowc::1CbKgt92SDCKteDSUwIVqg
Create a Google AI Studio Inference Endpoint PUT /_inference/{{taskType}}/{{googleaistudioInferenceId}} conn_mod_def::GKBWxsMvVPQ::jPpWlt41RWuqt5KpVbEa4A
Create a Google Vertex AI Inference Endpoint PUT /_inference/{{taskType}}/{{googlevertexaiInferenceId}} conn_mod_def::GKBWvf9P0Ik::hNiEgeywRsy-u5pFBRHqDg
Create a Groq Inference Endpoint PUT /_inference/{{taskType}}/{{groqInferenceId}} conn_mod_def::GKBWvn4o5NY::JaQN3ZwfQ5qR5SMF23mDmg
Create a Hugging Face Inference Endpoint for a Task PUT /_inference/{{taskType}}/{{huggingfaceInferenceId}} conn_mod_def::GKBWvwSFIYI::MBRjwXMgTpaMcHR1iM7SsQ
Create a JinaAI Inference Endpoint PUT /_inference/{{taskType}}/{{jinaaiInferenceId}} conn_mod_def::GKBWyELJdf4::2iL3MXoTSAOqffafPnUP7w
Create a Llama Inference Endpoint PUT /_inference/{{taskType}}/{{llamaInferenceId}} conn_mod_def::GKBWv4XnHRc::7P2eW5xMRvOC3HnzYpn1FQ
Create a Mistral Inference Endpoint PUT /_inference/{{taskType}}/{{mistralInferenceId}} conn_mod_def::GKBWwH_2OTs::1QsgvpZRTbmccQzqfYiUfw

Read the full file on GitHub · 185 lines

Changes

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.

  1. 6d ago First seen · 185 lines · 0 tokens per session scan A 79476027fc0b

Subscribe to this mod's changes

elasticsearch is a skill published in the GitHub repository withoneai/one-agent-plugin (1 stars, last pushed 20d ago), licensed MIT. It adds 108 tokens to every session and 5,830 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to 2-chat, differing in 324 lines, and is treated as a copy.

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