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LittleSongxx
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GitHub

github-deep-research

Automate GitHub repository research and generate structured markdown reports.

2|1|Updated May 11, 2026
Search Tags:#code analysis#github#report generation#data analysis#repository analysis
One-click install
npx skills add https://github.com/LittleSongxx/SoulSearcher --skill github-deep-research-littlesongxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: github-deep-research
Source: https://github.com/LittleSongxx/SoulSearcher/tree/main/skills/public/github-deep-research
Command: npx skills add https://github.com/LittleSongxx/SoulSearcher --skill github-deep-research-littlesongxx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, requests-futures, python-dateutil, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the problem of manually conducting deep research on GitHub repositories by automating the process, providing a structured report with timeline reconstruction, metrics analysis, and competitive comparisons.

Core Features & Use Cases

  • Automated Research Workflow: Conducts multi-round deep investigation using GitHub API, web_search, and web_fetch.
  • Structured Reports: Generates comprehensive markdown reports with timeline reconstruction, metrics analysis, and competitive comparisons.
  • Use Case: Imagine you need to analyze a popular open-source project. Use this Skill to automatically gather data from GitHub, perform an in-depth analysis, and generate a structured report.

Quick Start

Use the github-deep-research skill to analyze the repository 'owner/repo'.

Frequently Asked Questions about github-deep-research

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

FAQPage Schema
How do I automate GitHub repository analysis and generate a structured report?▼

Automate GitHub repository analysis by using scripts to interact with the GitHub API, perform web searches, and analyze content. This generates a structured markdown report with timeline reconstruction, metrics, and competitive comparisons.

Can I conduct deep research on a GitHub repository using Python and API interactions?▼

Yes, you can conduct deep research on a GitHub repository using Python scripts and API interactions. The automated workflow performs multi-round investigation using GitHub API, web_search, and web_fetch to gather and analyze repository data.

What is the best way to reconstruct the timeline and metrics of an open-source project?▼

The best way to reconstruct the timeline and metrics of an open-source project is to automate data gathering through GitHub API access. This process produces structured reports containing timeline reconstruction, metrics analysis, and competitive comparisons.

Does GitHub deep research require Python and specific dependencies to run?▼

Yes, GitHub deep research requires Python for scripting and specific dependencies including requests, requests-futures, and python-dateutil. You also need GitHub API access to automate the repository analysis workflow.

What competitive comparisons can I generate when analyzing GitHub repositories?▼

When analyzing GitHub repositories, you can generate competitive comparisons within a comprehensive markdown report. The structured output includes timeline reconstruction and metrics analysis to evaluate the repository against similar projects.

Why does automated repository analysis use web search alongside GitHub API data?▼

Automated repository analysis uses web search alongside GitHub API data to conduct multi-round deep investigation. Combining API interaction, web_search, and web_fetch enables comprehensive content analysis and structured report generation.

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