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search-strategy

Decompose natural language queries into parallel, source-specific searches across connected data sources.

1|Updated Mar 30, 2026
Search Tags:#multi-source#search#strategy#orchestration#semantic-search#knowledge-base#query-decomposition
One-click install
npx skills add https://github.com/ilove323/comlan-skills --skill search-strategy-ilove323
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: search-strategy
Source: https://github.com/ilove323/comlan-skills/tree/main/enterprise-search/skills/search-strategy
Command: npx skills add https://github.com/ilove323/comlan-skills --skill search-strategy-ilove323

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Decomposes a natural language query into parallel, source-specific searches across connected data sources, enabling fast, coherent answers from distributed information.

Core Features & Use Cases

  • Query decomposition: translate a question into per-source search components.
  • Parallel source orchestration: run semantic and keyword queries across chat, knowledge bases, issue trackers, and documents.
  • Result fusion and ranking: merge results, deduplicate, and rank by relevance and freshness.
  • Ambiguity handling and fallbacks: identify ambiguity and apply fallback strategies to ensure useful outputs.
  • Use Case: when asked "What was the API migration decision?" the system searches chat logs, knowledge bases, and project trackers to synthesize a final answer.

Quick Start

Input your natural language question and let the tool decompose it into parallel, source-specific searches across connected sources.

Frequently Asked Questions about search-strategy

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

FAQPage Schema
How do I search across multiple knowledge bases and project trackers simultaneously?▼

This multi-source search strategy decomposes a natural language query into parallel, source-specific searches across connected knowledge bases and trackers, merging and ranking the fused results for a coherent answer.

What is query decomposition for semantic search?▼

Query decomposition for semantic search translates a single natural language question into multiple targeted search components, enabling parallel execution across distributed data sources like chat logs and document stores.

Can I run parallel searches across chat logs and document stores from one question?▼

Yes, parallel source orchestration takes a natural language input and runs concurrent semantic and keyword searches across connected chat logs, issue trackers, and document stores, then fuses and deduplicates the results.

How do I handle ambiguity when searching distributed data sources?▼

Ambiguity handling identifies unclear query components during multi-source search and applies fallback strategies, ensuring the orchestration process still produces useful, ranked outputs from connected knowledge bases.

What is the best way to synthesize answers from enterprise search systems?▼

Enterprise search synthesis is best achieved by decomposing queries into parallel searches across project trackers and knowledge bases, then applying result fusion and ranking by relevance and freshness to generate a final answer.

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