data-scraper-agent

Automate public data collection and enrichment from websites and APIs.

3|2|Updated Mar 8, 2026

Search Tags:#supabase #notion #workflow-automation #sheets #web-scraping #data-scraping #ai-enrichment

Ready to run

npx skills add https://github.com/agentmatters/mullai-bot --skill data-scraper-agent-agentmatters

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-scraper-agent
Source: https://github.com/agentmatters/mullai-bot/tree/main/src/Mullai.Skills/Skills/claude-code-everything/data-scraper-agent
Command: npx skills add https://github.com/agentmatters/mullai-bot --skill data-scraper-agent-agentmatters

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Public data collection across websites and APIs is time-consuming, error-prone, and hard to scale; this Skill automates the end-to-end pipeline from scraping to AI enrichment and storage, reducing manual effort dramatically.

Core Features & Use Cases

End-to-end data scraping from public sources (websites and APIs) with configurable scope
AI-assisted enrichment and scoring to turn raw items into actionable insights
Flexible storage to Notion, Google Sheets, or Supabase, with learning from user feedback to improve results over time
Use Case: monitor job boards, price trackers, or news feeds, enrich each item, and store it for review or automation

Quick Start

Edit config.yaml to configure sources and AI preferences, then run the scraper to fetch, enrich, and store results.

Frequently Asked Questions about data-scraper-agent

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

FAQPage Schema

How do I automate web scraping and store the results in Notion or Google Sheets?▼

Automating web scraping and storing results in Notion or Sheets requires a configurable pipeline that fetches public data, enriches it with AI, and exports it directly to your chosen backend. You define sources and storage targets in a configuration file to run the end-to-end workflow.

What is AI data enrichment and how does it work with scraped web data?▼

AI data enrichment processes raw scraped items from websites or APIs using a free LLM to score and transform them into actionable insights. This pipeline handles enrichment automatically after data collection, improving output quality by learning from your feedback over time.

Can I schedule batch processing for scraping public APIs and websites?▼

Scheduling batch processing for scraping public APIs and websites is supported through a configurable pipeline. You can automate runs to fetch, enrich, and store data in Supabase or other backends, handling deduplication and model fallback automatically during scheduled execution.

Does this data scraping pipeline work with Supabase for storing enriched datasets?▼

This data scraping pipeline works with Supabase by providing flexible storage backend configurations for enriched datasets. It supports storing processed items directly into Supabase alongside alternatives like Notion and Google Sheets, allowing seamless integration into your existing data stack.

How do I configure workflow automation for monitoring job boards and price trackers?▼

Configuring workflow automation for monitoring job boards or price trackers involves editing a configuration file to specify public sources and AI preferences. The pipeline then automatically fetches updates, enriches the data, and stores it for review.

What are the limitations of using a free LLM for data enrichment in a scraping pipeline?▼

Limitations of using a free LLM for data enrichment include potential processing constraints or model availability issues, mitigated by built-in model fallback support. The pipeline relies on this free AI to score raw items, so complex reasoning tasks may require tuning via user feedback to improve accuracy.