index-design

Design Elasticsearch index mappings and settings for search and query performance.

6|Updated Feb 20, 2026

Search Tags:#validation #sharding #elasticsearch #mapping #analyzer #ilm #index-design

Ready to run

npx skills add https://github.com/patrykkopycinski/elastic-cursor-plugin --skill index-design

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: index-design
Source: https://github.com/patrykkopycinski/elastic-cursor-plugin/tree/main/.cursor/skills/index-design
Command: npx skills add https://github.com/patrykkopycinski/elastic-cursor-plugin --skill index-design

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guide developers and operators to design and optimize Elasticsearch index mappings, settings, and templates to improve search relevance, storage efficiency, and query performance.

Core Features & Use Cases

Use case clarification: Elicit whether the index is for search, vectors, logs, or metrics and capture query patterns and scale.
Mapping & analyzer guidance: Recommend field types (keyword, text, numeric, date, nested, dense_vector) and appropriate analyzers for full-text search.
Operational recommendations: Advise on shard counts, refresh_interval, index templates, ILM for time-based data, and validation steps including sample indexing and representative queries.
Integration steps: Explain when to use dynamic mappings, runtime fields, copy_to, and how to apply mappings with create_index or update templates.

Quick Start

Use the index-design skill to analyze my Elasticsearch use case and generate recommended mappings, settings, and validation steps.

Frequently Asked Questions about index-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design Elasticsearch index mappings for full-text search and storage efficiency?▼

Designing Elasticsearch index mappings involves selecting appropriate field types like text or keyword, configuring analyzers for full-text search, and applying shard and refresh settings to optimize both search relevance and storage efficiency.

What is the best way to configure Elasticsearch settings and ILM for time-series data?▼

Configuring Elasticsearch settings for time-series data involves applying index templates with Index Lifecycle Management (ILM) policies, optimizing shard counts, and setting refresh intervals to manage rolling indices for metrics or logs.

When do I need to use dense_vector field mappings in Elasticsearch?▼

You need dense_vector field mappings in Elasticsearch when building vector search scenarios, enabling storage and retrieval of high-dimensional vectors alongside traditional text or keyword fields for hybrid search.

Can I use dynamic mappings and runtime fields to refine an existing Elasticsearch index?▼

Yes, you can use dynamic mappings and runtime fields to refine an existing Elasticsearch index, enabling flexible schema evolution and query changes without immediate reindexing, validated through sample indexing and representative queries.

Does Elasticsearch mapping design require validation steps like sample indexing?▼

Elasticsearch mapping design requires validation steps including sample indexing and representative query tests to confirm that field types, analyzers, and shard configurations deliver expected search relevance and query performance.