research-expert

Gather structured research outputs with sources across domains.

Updated Jan 9, 2026
One-click install
npx skills add https://github.com/diendh/Ferro-VPN --skill research-expert-diendh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research-expert
Source: https://github.com/diendh/Ferro-VPN/tree/main/.agent/skills/research-expert
Command: npx skills add https://github.com/diendh/Ferro-VPN --skill research-expert-diendh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Focused, structured gathering of information to answer complex research questions without manual sifting and synthesis.

Core Features & Use Cases

  • Mode-based research workflow: automatically detects the task mode (quick check, focused investigation, or deep research) and adapts the data collection plan.
  • Structured output: produces an organized findings report with sources, summaries, and gaps.
  • Scalable across domains: applicable to literature reviews, competitive analysis, requirements discovery, and knowledge mapping.

Quick Start

Provide a topic for research and desired depth, and obtain a structured, sourced report.

Frequently Asked Questions about research-expert

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

FAQPage Schema
What is structured information gathering for complex research questions?

Mode-based research workflows automatically detect the required depth—quick check, focused investigation, or deep research—and adapt the data collection plan accordingly. This ensures the parallel processing and source evaluation scale efficiently for your specific topic.

How do I conduct a competitive analysis or literature review without manual sifting?

Structured research outputs are highly scalable and applicable to literature reviews, competitive analysis, requirements discovery, and knowledge mapping. The mode-detection mechanism adapts the data collection plan to fit these diverse domains effectively.

Does parallel web search work for requirements discovery and knowledge mapping tasks?

Yes, parallel web search supports requirements discovery and knowledge mapping by running scalable data collection across domains. It automatically detects the task mode and applies focused source evaluation to deliver curated, structured research outputs.

What are the limitations of automated source evaluation in structured research outputs?

Automated source evaluation limitations depend on web search accessibility and the available evidence for the requested topic. While parallel processing gathers information efficiently, the final structured report may highlight unresolved knowledge gaps where source data is missing.