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skill-arch-search

Design scalable search architectures with indexing pipelines, query DSL, and ranking strategies.

4|Updated Mar 31, 2026
Search Tags:#architecture#indexing#search#autocomplete#ranking#full-text#facets
One-click install
npx skills add https://github.com/ryan-nguyen-01/agent-platform --skill skill-arch-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-arch-search
Source: https://github.com/ryan-nguyen-01/agent-platform/tree/main/.claude/skills/skill-arch-search
Command: npx skills add https://github.com/ryan-nguyen-01/agent-platform --skill skill-arch-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a blueprint for designing and implementing scalable search architectures that support data ingestion, indexing, and fast, relevant querying across multiple data sources.

Core Features & Use Cases

  • End-to-end indexing pipeline design (data sources → index → search index → API → client) with real-time or near-real-time updates.
  • Full-text search, advanced ranking, facets, and autocomplete for catalogs, documents, and user-generated content.
  • Use Case: build a multi-source search service that supports dynamic filtering, personalized results, and fast suggestions.

Quick Start

Outline a scalable search design for a new data platform and an initial indexing pipeline.

Frequently Asked Questions about skill-arch-search

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

FAQPage Schema
How do I design a scalable search architecture for multi-source data ingestion?▼

Designing scalable search architecture requires mapping diverse data sources to a document model via an indexing pipeline, enabling efficient real-time querying and ranking across the search index.

What is the best way to implement full-text search with faceted filtering and autocomplete?▼

Full-text search with facets and autocomplete requires defining specific text analyzers, a query DSL, and aggregation strategies within your indexing pipeline to support dynamic filtering and fast suggestions.

How do I build an indexing pipeline for near-real-time updates to a product catalog?▼

Building an indexing pipeline for near-real-time updates involves connecting data sources directly to your search index, ensuring product catalog changes are quickly processed and available for querying.

Does this search architecture design support personalized ranking strategies for user-generated content?▼

Yes, the search architecture design supports personalized ranking strategies by specifying advanced ranking mechanisms within the query DSL, allowing tailored results for user-generated content and product catalogs.

When should I use a query DSL for aggregations instead of standard filtering?▼

Use a query DSL for aggregations when your search architecture requires complex faceted filtering and dynamic ranking across large datasets, which standard filtering cannot efficiently process.

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