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storm-research

Synthesize multi-perspective expert conversations with adversarial fact-checking into cited research briefs.

Updated Apr 17, 2026
Search Tags:#architecture#research#verification#analysis#strategy#synthesis
One-click install
npx skills add https://github.com/xinye1/xl-skills --skill storm-research-xinye1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: storm-research
Source: https://github.com/xinye1/xl-skills/tree/main/skills/storm-research
Command: npx skills add https://github.com/xinye1/xl-skills --skill storm-research-xinye1

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This skill solves the problem of shallow, unverified, or biased research by automating a rigorous, multi-perspective investigation process that produces both a comprehensive article and a machine-actionable implementation brief.

Core Features & Use Cases

  • Multi-perspective discovery: Automatically identifies and adopts diverse expert roles to explore a topic from multiple angles.
  • Adversarial verification: Uses a 3-vote adversarial system to refute claims, ensuring high-confidence, source-grounded output.
  • Implementation-ready output: Generates a structured Implementation Brief alongside the article, making the research immediately useful for downstream AI agents or developers.
  • Use Case: Use this when you need to research a complex technical architecture, compare product strategies, or investigate a data pipeline, where you need to be certain of the facts before building.

Quick Start

Use the storm-research skill to investigate the pros and cons of adopting a vector database for our new search service.

Frequently Asked Questions about storm-research

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

FAQPage Schema
How do I conduct deep research for software architecture decisions without getting biased results?▼

Software architecture research requires synthesizing multi-perspective expert conversations and applying adversarial fact-checking to ensure high-confidence, source-grounded findings free from shallow or biased conclusions.

What is the best way to verify technical research before building a data pipeline?▼

Verifying technical research uses a 3-vote adversarial system to refute claims, ensuring high-confidence, source-grounded output before you build a data pipeline or implement complex technical architecture.

Can I generate machine-parseable implementation briefs from product strategy research?▼

Generating machine-parseable implementation briefs from product strategy research synthesizes expert conversations to produce structured output immediately useful for downstream AI agents or developers.

Does multi-perspective research work for comparing complex technical architectures?▼

Multi-perspective research compares complex technical architectures by automatically identifying and adopting diverse expert roles to explore the topic from multiple angles, yielding comprehensive and cited findings.

When should I use adversarial fact-checking for technical decision-making?▼

Adversarial fact-checking for technical decision-making is used when you need to be certain of the facts before building, such as when investigating a vector database adoption or evaluating a new search service strategy.

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