Fetches and ranks WeChat articles based on research interests with seamless Obsidian integration.
This skill automates the discovery and curation of technical content from WeChat Official Accounts, specifically tailored for researchers and developers. It intelligently ranks articles using a multi-tier priority system focusing on AI Agents, System Design, and GitHub projects. By providing automated date filtering, quality assessment, and direct export to Obsidian vaults in structured Markdown, it transforms raw social media content into a searchable, categorized knowledge base for daily research and academic tracking.
Key Features
01Direct Obsidian integration with structured frontmatter, internal links, and tagging.
020 GitHub stars
03Quality-based filtering to remove marketing noise, ads, and low-value content.
04Flexible fetching from specific accounts or all followed subscriptions.
05Automated date filtering for daily summaries or historical research.
06Intelligent ranking based on specific research interests like AI Agents and System Design.
Use Cases
01Automated discovery and cataloging of trending GitHub open-source projects.
02Thematic literature reviews across multiple WeChat technical blogs for system design insights.
03Automated daily morning research briefings focused on the latest AI and LLM trends.
