research

Plan and conduct systematic technical research with structured reporting.

3|2|Updated Dec 2, 2025
One-click install
npx skills add https://github.com/hotriluan/ai-command-center --skill research-hotriluan
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/hotriluan/ai-command-center/tree/main/research
Command: npx skills add https://github.com/hotriluan/ai-command-center --skill research-hotriluan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Conducting thorough research and synthesizing complex information into actionable insights can be a time-consuming and cognitively demanding task. This skill provides a structured methodology for performing in-depth research, evaluating sources, and generating comprehensive summaries, enabling AI to efficiently gather and process information.

Core Features & Use Cases

  • Structured Research Methodology: Guides the AI through defining research questions, identifying relevant sources, and systematically extracting key information.
  • Source Evaluation: Emphasizes critical evaluation of source credibility, bias, and relevance.
  • Insight Synthesis: Techniques for synthesizing disparate information, identifying patterns, and generating concise, actionable summaries.
  • Use Case: An AI needs to research the latest trends in serverless architecture. This skill can guide it to identify authoritative sources, extract key concepts from articles and whitepapers, compare different serverless platforms, and synthesize the findings into a comprehensive report, saving a human researcher hours of work.

Quick Start

Research the benefits and drawbacks of using microservices architecture for a new application.

Frequently Asked Questions about research

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

FAQPage Schema
How do I conduct systematic technical research for architecture and tool selection?

Systematic technical research involves defining clear research questions, identifying authoritative sources, extracting key information, validating findings across multiple sources, and synthesizing results into actionable implementation plans. This approach ensures decisions on architectures, tools, and scalability are evidence-based and maintainable.

What's the best way to evaluate source credibility when researching technology solutions?

Evaluate source credibility by assessing the author's expertise, publication venue reputation, recency of information, potential bias, and relevance to your specific use case. Cross-referencing findings across multiple independent sources strengthens confidence in your conclusions.

How do I synthesize information from multiple sources into a comprehensive research report?

Synthesize by identifying common patterns and themes across sources, noting areas of agreement and disagreement, extracting key concepts, and organizing findings into structured sections addressing your original research questions. This produces actionable insights rather than disconnected facts.

Can I use research methodology to compare competing platforms or architectural approaches?

Yes. Structured research methodology supports comparative analysis by systematically gathering requirements, evaluating each option against defined criteria, documenting trade-offs, and validating claims through multiple sources. This produces justified recommendations aligned with YAGNI, KISS, and DRY principles.

What information should I gather when researching a new technical stack or framework?

Gather performance benchmarks, security track record, community maturity, maintenance status, integration compatibility, scalability limits, and real-world implementation case studies. Phase-based scoping ensures you collect only relevant data for your specific phase—proof of concept, development, or production.

Why is critical thinking important when analyzing technical research findings?

Critical thinking prevents accepting biased or outdated claims uncritically. It involves questioning source motivations, identifying gaps in evidence, recognizing evolving best practices, and connecting findings to your constraints. This rigor produces decisions resilient to future change.