index-design

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.