Detect trending topics by analyzing entity co-occurrence and cross-source mentions.
npx skills add https://github.com/X-McKay/kubani --skill detect-trends
Please help me install this Agent Skill. Skill: detect-trends Source: https://github.com/X-McKay/kubani/tree/main/kubani/skills/news/analysis/detect-trends Command: npx skills add https://github.com/X-McKay/kubani --skill detect-trends
This Skill automates the identification of emerging and significant topics from a collection of news articles, helping users stay informed about the most relevant developments.
Use the detect-trends skill to find trending topics in recent news articles.
High-intent search queries and answers about installing and using this skill.
Trend detection analyzes entity co-occurrence and cross-source mentions across multiple articles to automatically identify emerging topics and classify their momentum as breaking, hot, rising, established, or fading.
Entity co-occurrence tracks how frequently specific entities appear together across multiple articles, enabling LLM analysis to identify emerging trends and classify their momentum as breaking, hot, rising, established, or fading.
LLM analysis evaluates the momentum of entity co-occurrence and cross-source mentions to categorize detected news trends into breaking, hot, rising, established, or fading classifications automatically.
Yes, you can perform news monitoring and trend detection without external dependencies; the skill uses built-in memory search for recent articles and LLM analysis to identify and store trends linked to source articles.
The best way to highlight breaking news stories is to analyze cross-source mentions and entity co-occurrence, classifying the momentum of detected topics to automatically surface the most significant developments across multiple sources.
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