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AgenticAIPlan
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datasets-search

Crawl dataset news from GitHub, blogs, and data portals into structured reports.

10|55|Updated Mar 31, 2026
Search Tags:#github#news#report#crawler#dataset#data-extraction
One-click install
npx skills add https://github.com/AgenticAIPlan/AgenticAISkills --skill datasets-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datasets-search
Source: https://github.com/AgenticAIPlan/AgenticAISkills/tree/main/skills/datasets-search
Command: npx skills add https://github.com/AgenticAIPlan/AgenticAISkills --skill datasets-search

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Crawls and aggregates dataset news from multiple sources, extracts key metadata, and generates structured reports to keep teams updated on data resources, quality signals, and industry trends.

Core Features & Use Cases

  • Multi-source crawling: fetches dataset news from GitHub, technical media, and data portals.
  • Smart extraction: identifies dataset name, company, description, links, and quality signals.
  • Incremental reporting: outputs weekly delta reports to track new datasets and certifications.

Quick Start

Run python3 scripts/dataset_crawler.py --output weekly_report.md to generate a structured dataset news report.

Frequently Asked Questions about datasets-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate crawling and reporting of dataset news from multiple sources?▼

You can automate dataset news crawling and reporting by running a Python script that fetches updates from GitHub, technical media, and data portals, then compiles a structured markdown report.

How does incremental weekly reporting for newly discovered datasets work?▼

Incremental weekly reporting works by applying deduplication to crawled dataset news, extracting metadata like dataset names and companies, and outputting a delta report that tracks only new datasets and certifications.

Can I use this to extract dataset names and company information from GitHub and data portals?▼

Yes, smart extraction identifies dataset names, company names, descriptions, links, and quality signals from GitHub and data portals, expanding company names and applying deduplication to ensure structured outputs.

Do I need the requests library to run the dataset crawler script?▼

Yes, the requests library is required as a dependency to execute the Python crawler script that fetches dataset news and generates the structured weekly report.

What is the best way to track data resources and industry trends for a data-science workflow?▼

The best way is to run an automated crawler that aggregates dataset news, extracts quality signals, and generates weekly delta reports to keep research teams updated on data resources and industry trends.

Are there limitations when configuring multiple data sources for automated dataset extraction?▼

Limitations depend on the accessibility and structure of the configured data sources; the crawler supports robust fetching from GitHub, blogs, and data portals, but source-specific anti-scraping measures may impede extraction.

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