Poll RSS feeds, GitHub repositories, and JSON APIs for new items.
npx skills add https://github.com/projectedanx/hermes-agent --skill watchers-projectedanx
Please help me install this Agent Skill. Skill: watchers Source: https://github.com/projectedanx/hermes-agent/tree/main/optional-skills/devops/watchers Command: npx skills add https://github.com/projectedanx/hermes-agent --skill watchers-projectedanx
This Skill solves the problem of information overload by automating the monitoring of external data sources and alerting you only when new, relevant content appears.
Ask the agent to watch the RSS feed at https://news.ycombinator.com/rss every 15 minutes and summarize any new headlines it finds.
High-intent search queries and answers about installing and using this skill.
You can monitor RSS feeds and GitHub repositories by setting up automated polling with a persistent watermark state. This tracks previously seen data across execution intervals, filtering out old items and reporting only new updates.
To prevent duplicate notifications when polling JSON APIs, the monitoring process maintains a persistent watermark state. This deduplication mechanism tracks seen items across intervals, ensuring only newly detected data triggers an alert.
Yes, you can schedule automated RSS and JSON API monitoring using cron jobs. The polling scripts integrate seamlessly with cron, allowing you to execute periodic checks and deliver update summaries directly to your interface.
No external Python libraries are required for parsing RSS feeds and JSON APIs. The monitoring scripts rely entirely on the standard Python library to perform HTTP requests, XML parsing, and JSON parsing for automated tracking.
The best way to track new GitHub releases without manual checks is automated polling. By maintaining a persistent watermark, the system detects new repository items and reports them directly, eliminating the need to visit the site.
If your polling script reports old RSS feed items, the persistent watermark state is likely failing to save or load correctly. This watermark is essential for filtering out previously seen data across execution intervals to prevent duplicate alerts.
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