deep-research

Automate structured multi-source research and generate evidence-based reports.

Updated Aug 23, 2026

Search Tags:#templates #analysis #multi-source #deep-research #serper_search #web_crawler #python_execute

Ready to run

npx skills add https://github.com/alishangtian/proteus-ai --skill deep-research-alishangtian

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/alishangtian/proteus-ai/tree/main/proteus/docker/volumes/agent/skills/deep-research
Command: npx skills add https://github.com/alishangtian/proteus-ai --skill deep-research-alishangtian

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill enables professional researchers to transform disparate sources into coherent, evidence-based insights by enforcing systematic gathering, validation, synthesis, and reporting workflows.

Core Features & Use Cases

Systematic Information Gathering: Guides multi-source collection across academic, industry, and official sources.
Validation & Synthesis: Applies cross-source verification, structured analysis, and data-driven conclusions.
Professional Reporting: Outputs comprehensive reports using templates with clearReferences and data visualizations.
Use Case: Ideal for market analyses, technology evaluations, and literature reviews requiring credible documentation.

Quick Start

Provide a topic and the tool will generate a structured, multi-source research report with citations.

Frequently Asked Questions about deep-research

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

FAQPage Schema

How do I automate multi-source deep research for an evidence-based report?▼

Automate multi-source deep research by using structured workflows that guide information gathering, cross-source validation, and synthesis to output evidence-based reports with citations and visualizations.

What is the best way to synthesize academic literature reviews and market research data?▼

Synthesize academic literature reviews and market research by applying cross-source verification and data-driven analysis using built-in templates and modular tools to generate professional documentation.

Can I use Python and pandas for data analysis within a structured research workflow?▼

Yes, you can execute Python scripts leveraging pandas, numpy, and scipy within the workflow to perform data-driven analysis and generate visualizations using matplotlib and seaborn for your research reports.

Does this deep research workflow support automated web crawling and searching?▼

Yes, the workflow integrates web crawler and search capabilities to automate systematic information gathering across multiple disparate academic, industry, and official online sources for validation.

How to generate professional research reports with citations from disparate sources?▼

Generate professional research reports by transforming disparate collected sources into coherent insights through built-in reporting templates that enforce clear referencing and include data visualizations.

What are the limitations of using automated templates for technology evaluation research?▼

Automated templates enforce systematic structure but require clear topic inputs and rely on the quality of multi-source web crawling, meaning complex technology evaluations still need human validation for accurate conclusions.