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Elasticsearch Best Practices

Checked
mindrally·vlatest·database·Apache-2.0

Helps with Elasticsearch, best practices as part of agent workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mindrally/skills --skill elasticsearch-best-practices -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mindrally

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7682ca7771
Manifest
elasticsearch-best-practices/SKILL.md
Scanned
1 file, 539 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides guidance and best practices for designing, configuring, and optimizing Elasticsearch deployments. It covers key areas like index design (including mappings), shard sizing, cluster configuration, security, query optimization, and index lifecycle management. The goal is to help users build efficient and reliable Elasticsearch solutions that meet their specific performance and data storage needs.

When to use it

  • When designing a new Elasticsearch index or mapping.
  • To troubleshoot slow search queries in an existing Elasticsearch cluster.
  • For optimizing shard sizing and cluster configuration for improved performance.
  • To implement security measures and access controls within Elasticsearch.
  • To automate the lifecycle management of indices, including rollover, shrinking, and deletion.

Key capabilities

  • Index Design & Mapping: Defining explicit mappings with appropriate data types (keyword, text, date, numeric, boolean, geo_point, nested).
  • Query Optimization: Guidance on using match queries and other query types effectively.
  • Shard Sizing: Recommendations for optimal shard size and the number of shards per heap.
  • Cluster Configuration: Best practices for configuring cluster settings like number_of_shards and number_of_replicas.
  • Index Lifecycle Management (ILM): Strategies for managing index lifecycle phases (hot, warm, delete) including rollover, shrinking, and merging.
  • Analysis Configuration: Defining custom analyzers with tokenizers and filters (e.g., synonym filtering).

Example prompts

  • "What's the best data type to use for a product ID field in Elasticsearch?"
  • "How should I configure shard sizing for an index containing time-series data?"
  • "Can you give me an example of an ILM policy that rolls over indices after 50GB or 7 days?"
  • "What are the key considerations when defining a custom analyzer in Elasticsearch?"

Tips & gotchas

  • Explicit Mappings: Always define explicit mappings to avoid unexpected behavior from dynamic mapping.
  • Shard Size: Target shard sizes between 20-40GB for optimal performance.
  • Data Types: Carefully select the appropriate data type for each field based on how it will be queried (keyword vs. text).