rails-search

What problem does it solve?

This Skill solves the pervasive issue of AI coding agents defaulting to overkill search solutions like Elasticsearch for Rails applications, even when simpler, more maintainable options like PostgreSQL full-text search are sufficient, leading to unnecessary operational overhead, non-idiomatic code, and wasted engineering time.

Core Features & Use Cases

• 3-Tier Search Guidance: Teaches the correct Rails search stack, defaulting to pg_search with PostgreSQL tsvector and trigram, stepping up to Meilisearch for typo tolerance and autocomplete, and only using Elasticsearch/OpenSearch for billion-row clustered scale.
• Production-Ready Implementation Patterns: Provides copy-paste ready examples for per-model search, multi-model search, precomputed tsvector columns for large tables, async background indexing, and index reconciliation to prevent drift between the database and search index.
• Anti-Pattern Guardrails: Explicitly blocks common mistakes like inline indexing in the request cycle, over-engineering with Elasticsearch for small datasets, and indexing unfiltered user input as the sole ranking source.
• Use Case: If you need to add search to a 100k-post Rails blog, this Skill will guide you to use pg_search with a GIN index instead of provisioning a full Elasticsearch cluster, eliminating unnecessary operational overhead.

Quick Start

Use the rails-search skill to add optimized full-text search to your Rails application, selecting the appropriate search tool tier based on your dataset size and feature requirements.