What problem does it solve?
Manually tracking, deduplicating, and ingesting relevant global news (geopolitics, AI policy, scientific breakthroughs, economic shifts) into a structured knowledge wiki is tedious, error-prone, and wastes hours of repetitive work each curation cycle.
Core Features & Use Cases
- Automated RSS Discovery: Pulls structured news data via Google News RSS across priority topics, eliminating reliance on unreliable defuddle parsing for mainstream news sources.
- Duplicate Prevention: Cross-checks new articles against an existing Article Index and recent headline files to avoid redundant ingestion of already indexed content.
- Structured Wiki Ingestion: Creates properly formatted wiki source pages with cross-links to existing research threads to turn raw news into actionable knowledge.
- Ongoing Monitoring: Uses a carryover file to track emerging themes, open research questions, and stories to monitor across cycles, with integrated kanban review for unresolved items.
- Use Case: Knowledge managers, researchers, or wiki maintainers can automate their entire news curation pipeline from discovery to wiki ingestion, ensuring consistent, non-redundant knowledge base updates.
Quick Start
Use the news-agent skill to curate and ingest 3-5 relevant global news stories from your priority topics into your LLM-WIKI knowledge base for this cycle.