search-engine

Provide full-text search with faceted navigation and log analytics.

Updated Jun 5, 2026

Search Tags:#full-text search #search engine #log analytics #faceted navigation #event analytics

Ready to run

npx skills add https://github.com/hung-phan/system-skills --skill search-engine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: search-engine
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/data-systems/search-engine
Command: npx skills add https://github.com/hung-phan/system-skills --skill search-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires elasticsearch, opensearch, solr, meilisearch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of implementing fast, scalable, and accurate search capabilities in applications that require full-text search, faceted navigation, and log/event analytics at scale.

Core Features & Use Cases

Full-Text Search: Enables relevance ranking, stemming, fuzzy matching, phrase queries, and autocomplete for user queries.
Faceted Navigation: Supports aggregate counts grouped by category, brand, price-bucket, etc.
Log/Event Analytics: Handles large-scale event and log data with tools like Elasticsearch, OpenSearch, Solr, and Meilisearch.
Use Case: A content management system needs to provide users with the ability to search through millions of documents efficiently, with filters for various categories and metadata.

Quick Start

Use the search-engine skill to index and search documents within your system, leveraging full-text search and faceted navigation capabilities.

Frequently Asked Questions about search-engine

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

FAQPage Schema

How do I implement full-text search with faceted navigation for a large-scale application?▼

Full-text search with faceted navigation for scalable applications is implemented using search engines like Elasticsearch, OpenSearch, Solr, or Meilisearch to provide relevance ranking and aggregate category counts.

Can I use Elasticsearch or OpenSearch for log and event analytics?▼

Yes, Elasticsearch and OpenSearch support large-scale log and event analytics by handling large volumes of event data, optimized for recall-and-rank workloads within read-mostly system architectures.

What is the best way to add fuzzy matching and autocomplete to user search queries?▼

The best way to add fuzzy matching and autocomplete to user search queries is by utilizing a dedicated search engine that provides built-in relevance ranking and phrase query capabilities.

How do I index millions of documents efficiently while supporting category filters?▼

Index millions of documents efficiently by leveraging a search engine designed for recall-and-rank workloads, enabling fast retrieval and aggregate counts grouped by metadata like category or brand.

Does Meilisearch work well for complex search requirements in read-mostly workloads?▼

Yes, Meilisearch works well for complex search requirements in read-mostly workloads, offering full-text search functionality and faceted navigation optimized for scalable applications.

When should I not use a dedicated search engine for application queries?▼

Avoid using a dedicated search engine if your application lacks large-scale data volumes or complex search requirements, as these systems are optimized specifically for recall-and-rank workloads.