pip install feedparser python-dateutil

// npm install rss-parser
const Parser = require('rss-parser');
const parser = new Parser();

const SOURCES = {
  Reuters: 'https://feeds.reuters.com/Reuters/worldNews',
  AP: 'https://feeds.apnews.com/apnews/topnews',
  BBC: 'http://feeds.bbci.co.uk/news/world/rss.xml',
  'Google News': 'https://news.google.com/rss/search?q=world'
};

async function fetchRecent(days = 3) {
  const cutoff = Date.now() - days * 24 * 60 * 60 * 1000;
  const all = [];

  for (const [source, url] of Object.entries(SOURCES)) {
    const feed = await parser.parseURL(url);
    for (const item of feed.items || []) {
      const ts = new Date(item.pubDate || item.isoDate || 0).getTime();
      if (!ts || ts < cutoff) continue;
      all.push({ source, title: item.title || '', link: item.link || '', ts });
    }
  }

  return all.sort((a, b) => b.ts - a.ts);
}

// Next step: add title-similarity clustering (same idea as Python section above)

Use the News Aggregation skill.

Requirements:
1) Pull news from multiple predefined sources (news sites + aggregators).
2) Default to only the last 3 days unless user asks another time range.
3) Group similar headlines into one short topic.
4) Under each topic, list all main source links (not just one source).
5) If 3+ sources cover the same event, output one topic with all those links.
6) Keep summaries short and factual; avoid adding unsupported claims.