last30days-skill — What is it?

mvanhorn/last30days-skill is an AI-driven research tool that aggregates and synthesizes information from various social media and web platforms, providing users with a comprehensive summary of recent discussions and trends.

⭐ 26,077 Stars 🍴 2,210 Forks Python Author: mvanhorn
Source: README View on GitHub →

Why it matters

This project is trending due to its unique approach to information aggregation, leveraging real-world engagement metrics like upvotes, likes, and real money from platforms like Reddit, X, YouTube, and Polymarket. Its ability to bridge the gap between isolated platforms and provide a unified view of online discussions is particularly appealing to users seeking real-time, actionable insights.

Source: README

Core Features

Multi-source Aggregation

Collects and synthesizes information from Reddit, X, YouTube, HN, Polymarket, and the web, scoring content based on real engagement metrics.

Source: README
AI Synthesis

Utilizes an AI agent to create a grounded summary of the collected information, providing a concise and relevant overview.

Source: README
Shareable HTML Briefs

Generates self-contained HTML briefs that can be easily shared and accessed offline, enhancing collaboration and knowledge sharing.

Source: README
Intelligent Search

Understands the context of the search query and searches the appropriate sources and communities, improving the relevance of the results.

Source: README

Architecture

The architecture of mvanhorn/last30days-skill is modular, with distinct components for data collection, processing, and presentation. It leverages various APIs for data retrieval and employs an AI agent for synthesis. The codebase is structured into modules for different platforms and functionalities, indicating a clear separation of concerns.

Source: Code tree

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) requests pytest pytest-cov Multi-source AggregationMulti-source Aggreg… AI Synthesis Shareable HTML BriefsShareable HTML Brie… Intelligent Search last30days-skill Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguagePythonFrameworkNot enough information
requestspytestpytest-cov
Not enough information
Source: Dependency files

Quick Start

1. Clone the repository: `git clone https://github.com/mvanhorn/last30days-skill.git` 2. Navigate to the repository directory: `cd last30days-skill` 3. Install dependencies: `pip install -r requirements.txt` 4. Run the setup wizard: `./setup.sh` 5. Use the skill: `/plugin marketplace add mvanhorn/last30days-skill` or `clawhub install last30days-official`
Source: README Installation/Quick Start

Use Cases

1. Researching recent trends and discussions on specific topics across various platforms. 2. Gaining insights into the latest developments in a particular field or industry. 3. Preparing for meetings or presentations by gathering relevant information from social media and other sources. 4. Monitoring the reputation and activities of individuals or companies.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Unique approach to information aggregation and synthesis.
  • Strength 2: Provides real-time, actionable insights.
  • Strength 3: Enhances collaboration and knowledge sharing with shareable HTML briefs.

Limitations

  • Limitation 1: Limited information on the technical stack and infrastructure.
  • Limitation 2: Unknown license may raise concerns for some users.
Source: Synthesis of README, code structure and dependencies

Latest Release

v3.2.1 (2026-05-10) - Added Digg AI 1000 source and HTML briefs.

Source: GitHub Releases

Verdict

mvanhorn/last30days-skill is a promising project for users seeking real-time, comprehensive insights from diverse online sources. Its unique approach to information aggregation and synthesis, combined with its ease of use, makes it a valuable tool for researchers, analysts, and professionals across various fields.

Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-05-24 13:03. Quality score: 85/100.

Data sources: README, GitHub API, dependency files