Collect and process RSS, web, and API data with deduplication and scheduling.
npx skills add https://github.com/huangrt01/CS-Notes --skill common-fetcher
Please help me install this Agent Skill. Skill: common-fetcher Source: https://github.com/huangrt01/CS-Notes/tree/main/.trae/openclaw-skills/common-fetcher Command: npx skills add https://github.com/huangrt01/CS-Notes --skill common-fetcher
This Skill provides a unified framework to collect, aggregate, and process diverse data sources (RSS, web, APIs) for AI-enabled analysis, reducing manual integration work.
Run the common-fetcher to fetch data from configured sources and generate the daily report.
High-intent search queries and answers about installing and using this skill.
You can automate RSS and web data collection for AI analysis using a centralized framework that aggregates sources with built-in scheduling. It processes diverse feeds automatically to supply ready data streams for AI scoring and summarization pipelines.
AI scoring and summarization for collected data streams is an automated process that categorizes, evaluates, and condenses information from RSS, web, and API sources. This mechanism transforms raw aggregated data into concise briefs and quick insights for research dashboards.
Yes, you can integrate multiple API sources for centralized industry monitoring through a modular architecture. This framework supports over 207 configured sources, enabling consistent data collection across diverse APIs to generate automated daily feeds and industry news reports.
Set up an environment-ready data pipeline with npm-based install by configuring the modular fetcher framework within your execution environment. This approach requires no external dependencies, allowing fast processing of archival data pipelines and research dashboards directly after installation.
The best way to generate daily research briefs from large data streams is using a unified fetcher framework with automated AI processing. It handles multi-source collection, deduplication, and summarization to deliver concise briefs without manual integration work.
Automated web scraping and data collection works efficiently for archival data pipelines through fast processing and modular architecture. The framework aggregates historical and real-time sources seamlessly, maintaining consistent data collection for long-term research and monitoring.
DaFumRun Node project healthchecks covering build, lint, and runtime issues.
i9wa4Generate daily GitHub activity reports and post them as issues.
spoonboboAggregate and score tech news from RSS feeds, GitHub releases, and web searches.
kettleofketchupOrchestrate Puppeteer CLI scripts for browser automation and JSON output.
