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deep-research

Synthesize and validate information from diverse sources into structured research reports.

1|Updated May 10, 2026
Search Tags:#research#multi-source#reporting#citation tracking#structured analysis
One-click install
npx skills add https://github.com/Cuki2910/M2G-NET --skill deep-research-cuki2910
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/Cuki2910/M2G-NET/tree/main/.agents/skills/deep-research
Command: npx skills add https://github.com/Cuki2910/M2G-NET --skill deep-research-cuki2910

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires search-cli, weasyprint, python>=3.9, jsonschema, argparse, hashlib, json, and includes scripts (resource) and references (resource) and templates (resource) and tests (resource) components.

What problem does it solve?

This Skill addresses the need for in-depth, well-documented research with structured reports, ensuring credibility and thoroughness in the analysis.

Core Features & Use Cases

  • Multi-Source Research: Aggregates information from multiple sources, including academic papers, industry reports, and news articles.
  • Structured Reporting: Generates detailed, structured reports with executive summaries, main analysis, synthesis, and conclusions.
  • Quality Assurance: Implements validation checks for citation accuracy, report structure, and content quality.

Quick Start

Run the 'deep research on the impact of artificial intelligence on healthcare' command.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I conduct multi-source research with citation tracking and structured reporting?▼

Multi-source research with citation tracking is conducted by aggregating information from academic papers, industry reports, and news articles, then synthesizing the data into structured reports with executive summaries, main analysis, and conclusions.

What is structured analysis in research reporting and when do I need it?▼

Structured analysis in research reporting synthesizes validated information from diverse sources into executive summaries, main analysis, and conclusions. You need it for complex research inquiries requiring thoroughness, credibility, and detailed evidence management.

Does Python 3.9 support the libraries needed for multi-source research and PDF generation?▼

Python 3.9 supports the required libraries for multi-source research and PDF generation, including weasyprint for formatting reports, jsonschema for validation, and search-cli for executing multi-provider searches and citation tracking.

How do I validate citation accuracy and content quality in a research report?▼

Citation accuracy and content quality in a research report are validated through implemented quality assurance checks that verify report structure, claim extraction, and evidence management using Python libraries like jsonschema and hashlib.

What's the best way to generate a structured PDF report from aggregated research data?▼

The best way to generate a structured PDF report from aggregated research data is by using weasyprint alongside Python libraries to format executive summaries, main analysis, and conclusions while maintaining citation tracking and evidence management.

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