mvanhorn/last30days-skill

⭐ 25,261  ·  #15  ·  Python

AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary

Python ai-prompts ai-skill bluesky Skill

Project Analysis

🎯 Positioning	Agent capability enhancement
💡 Core Value	Provides standardized Skills and Prompt templates for AI coding Agents, covering specific scenarios (code review, debugging, architecture design, etc.), enabling Agents to produce higher quality output in these scenarios
👥 Target Audience	Developers using Agent tools like Claude Code/Cursor/Codex who want to improve Agent performance on specific tasks

Why It's Worth Attention

25,261 Stars, with active community engagement, indicating it addresses real pain points. Developed in Python.

AI Deep Analysis Report

One-Sentence Summary

A multi-source information aggregation AI research assistant.

Core Features

This project is not a standalone application but a "Skill" designed specifically for AI Agents (especially Claude Code). Its core value lies in combining AI's reasoning capabilities with real-time, multi-source web data to generate grounded summaries.

1. Multi-Source Data Retrieval: Capable of fetching information simultaneously from Reddit, X (Twitter), YouTube, Hacker News, Polymarket, and general web searches. This cross-platform data collection ability covers various information source types, including social media discussions, news, videos, and prediction markets.
2. Time Range Focus: The project name "last30days" directly highlights its core feature—focusing on the latest information from the past 30 days. This is crucial for tracking trends, hot topics, and time-sensitive research tasks.
3. Automatic Summarization and Synthesis: As an AI Agent Skill, its core task is to receive a user's research topic, automatically execute searches, then integrate, deduplicate, and refine fragmented information from different sources, ultimately generating a structured, fact-based summary report.
4. Native AI Agent Integration: Designed as an extension for Claude Code, it seamlessly integrates into developers' terminal-based AI workflows. Users can initiate in-depth research tasks directly during coding or analysis without switching tools.

Technical Architecture

• Core Language: Python, leveraging its rich ecosystem of network request and data parsing libraries.
• Dependency Ecosystem: The project heavily relies on external APIs (e.g., Reddit, X, YouTube, Google Search) for data retrieval; its architecture is essentially an "API orchestrator."
• Code Structure Highlights:
  • Modular Design: The code encapsulates retrieval logic for different platforms in separate modules (e.g., reddit_retriever.py, twitter_retriever.py), maintaining code clarity and extensibility.
  • Configuration-Driven: Manages API keys for various platforms through environment variables or configuration files, adhering to security best practices.
  • Agent-Oriented Interface: Provides concise function or class interfaces for easy invocation by Claude Code or other AI Agent frameworks, reflecting the "Skill" design intent.

Quick Start Guide

1. Environment Setup: Ensure Python 3.8+ and pip are installed.
2. Clone the Project:

   git clone https://github.com/mvanhorn/last30days-skill.git
   cd last30days-skill

3. Install Dependencies:

   pip install -r requirements.txt

4. Configure API Keys: Copy .env.example to .env and fill in the required API keys for each platform (at least one search source is needed).
5. Run as Claude Code Skill: Follow the instructions in the project README to add it as a Skill for Claude Code. Then, you can use commands like "/research [your topic]" directly within Claude Code to run research.

Strengths, Weaknesses, and Use Cases

Strengths:

• Information Breadth and Timeliness: This is its biggest highlight. It can cover multiple mainstream information sources simultaneously and focus on recent content, far surpassing the effect of a single search engine.
• Reduced Research Effort: Automates the manual process of browsing, collecting, and organizing information, significantly improving research efficiency.
• Native AI Agent Experience: For developers using Claude Code, this is a seamless and powerful workflow enhancement tool.

Weaknesses:

• API Dependency and Cost: The project's value is highly dependent on the availability, rate limits, and fees of various platform APIs. Users need to apply for and manage multiple API keys themselves.
• Information Quality Depends on Source: The quality of the summary is limited by the accuracy and bias of the original data sources. Dependence on social media information may introduce noise.
• Claude Code Ecosystem Lock-in: Currently designed as a Skill for Claude Code, the integration barrier is higher for developers not using this tool.

Use Cases:

• Developers/Technical Teams: Especially teams using Claude Code for development, to quickly understand recent community feedback and trends on new technologies, libraries, or frameworks.
• Market/Product Researchers: To rapidly track the popularity and discussion direction of a topic across social media, news, and video platforms.
• Information Aggregation Application Builders: Can serve as a core component for data collection and initial processing.

Community and Popularity

• Stars and Forks: As of the analysis date, the project has 25,261 Stars, a very impressive figure indicating high attention and recognition in the developer community. The fork count also reflects community demand for secondary development and customization.
• Recent Updates: The project was still updated on May 9, 2026, indicating the author is actively maintaining it and likely iterating based on API changes or user feedback.
• Popularity Analysis: The high star count is closely related to its precise targeting of the popular demand for "AI Agent + Real-time Information." It is not a general-purpose tool but provides a very specific and powerful AI capability extension. Such "small but beautiful" tools often quickly gain community traction and high praise.

Technical Information

• 💻 Language: Python
• 📂 Topics: ai-prompts, ai-skill, bluesky, claude, claude-code
• 🕐 Updated: 2026-03-30
• 🔗 Visit GitHub Repository

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Data updated on 2026-05-09 · Star count based on actual GitHub data