Aggregate and deduplicate AI and technology news into static JSON datasets.
npx skills add https://github.com/alexaundre/mycc --skill ai-news-radar
Please help me install this Agent Skill. Skill: ai-news-radar Source: https://github.com/alexaundre/mycc/tree/main/.claude/skills/ai-news-radar Command: npx skills add https://github.com/alexaundre/mycc --skill ai-news-radar
Aggregating and maintaining a high-quality, de-duplicated stream of AI and technology news is time-consuming and error-prone when done manually; this Skill centralizes multi-source feeds, handles failures, and produces ready-to-publish static data so teams always have an up-to-date news timeline.
Run the update script to fetch the latest 24-hour AI news, write JSON outputs to the data directory, and serve the site locally.
High-intent search queries and answers about installing and using this skill.
To aggregate AI news from multiple RSS feeds into a static dataset, you can use a Python script with feedparser to ingest OPML lists, deduplicate content, and output ready-to-publish JSON files for static site hosting.
Yes, you can automate RSS news curation with GitHub Actions by scheduling the Python update script to run periodically, fetching a rolling 24-hour incremental update of AI news and writing the results to static JSON output files.
The best way to handle failed RSS feeds during news aggregation is to use a tool that automatically detects empty or broken sources, skips or replaces them, and emits diagnostic reports like failed_feeds and replaced_feeds for review.
Yes, OPML bulk ingestion supports bilingual title caching for AI news, allowing you to parse large subscription lists while caching Chinese and English titles to generate WaytoAGI-style daily or 7-day timeline exports.
You generate a static site from aggregated RSS technology news by running a Python update script that compiles a 24-hour incremental dataset into JSON outputs, which can then be deployed to static hosting platforms like Vercel or Netlify.
The Python dependencies required for OPML parsing and RSS feed aggregation include requests for fetching data, beautifulsoup4 for parsing, feedparser for reading feeds, and python-dateutil for managing rolling 24-hour date ranges.
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