query

Retrieve consolidated knowledge about people, companies, and ideas from natural-language questions.

1|Updated May 9, 2026
One-click install
npx skills add https://github.com/weiping/gbrain-cn --skill query-weiping
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: query
Source: https://github.com/weiping/gbrain-cn/tree/main/test/fixtures/openclaw-mixed-merge/skills/query
Command: npx skills add https://github.com/weiping/gbrain-cn --skill query-weiping

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The query skill helps you quickly discover and synthesize relevant information from a knowledge base when you know only the rough topic you want to learn about.

Core Features & Use Cases

  • Topic-based retrieval: Start with a natural-language question like what you know about a person, company, deal, or idea.
  • Fixture-ready regression stub: Provides a stable entry point for automated testing of the mixed-merge regression scenario without requiring full production logic.
  • Guided discovery prompt: Encourages consistent query phrasing aligned to the skill’s trigger pattern.

Quick Start

Ask: What do we know about Acme AI?

Frequently Asked Questions about query

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

FAQPage Schema
How do I retrieve and synthesize knowledge about an open-ended topic from my knowledge base?

To retrieve and synthesize knowledge about an open-ended topic, ask a natural-language question starting with "What do we know about" to discover consolidated context across people, companies, and ideas.

What is the best way to discover consolidated context for a company or person I only know roughly?

Discovering consolidated context for a company or person requires phrasing a guided discovery prompt like "What do we know about Acme AI," which triggers topic-based retrieval to synthesize relevant information into a single answer.

Can I use a natural-language question to get synthesized answers without writing a specific search query?

Yes, getting synthesized answers without a specific search query works by interpreting open-ended questions that start with "What do we know about," enabling knowledge discovery across broad subjects without requiring exact match terms.

Does the query skill require any dependencies or specific component configurations to function?

No dependencies or component configurations are required to use the query skill for information retrieval, as it operates with a minimal operational body and a deterministic, fixture-friendly query entry point using YAML metadata.

How does fixture-friendly regression testing work with knowledge discovery and synthesis prompts?

Fixture-friendly regression testing for knowledge discovery uses a stable entry point stub to validate mixed-merge regression scenarios, providing deterministic query phrasing aligned to the skill's trigger pattern without requiring full production logic.

When should I not use this approach for information retrieval and knowledge discovery?

You should not use this approach for information retrieval when you need complex boolean filtering or multi-hop reasoning across datasets, as it is designed for basic, open-ended topic synthesis rather than advanced relational queries.