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RonnasaydRonnasayd/aipp/skills/everything-claude-code/data-scraper-agent/SKILL.md

data-scraper-agent

Skill1 starsChanged 33 days agoNo licenceView on GitHub

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Runs on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.

  • Reads credentials
  • Installs packages
  • Commits and pushes

18 other projects use a version of this, 16 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use pytorch-patterns, make-interfaces-feel-better and 2 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Yog-SothoSkill

    pytorch-patterns

    PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Always activate when the user is writing PyTorch models or training scripts, reviewing deep learning code, debugging training loops or data pipelines, optimizing GPU memory or training speed, setting up reproducible experiments, implementing transfer learning or fine-tuning, or asks anything about nn.Module, DataLoader, autograd, AMP, or torch.compile.

    Also in 3 projects that use this

  • studio-lgtmSkill

    make-interfaces-feel-better

    Apply design engineering best practices to polish UI components - use when reviewing or improving interfaces, animations, typography, or interaction details.

    Also in 3 projects that use this

  • get-proofpilotSkill

    context-budget

    Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

    Also in 3 projects that use this

  • AnthropicSkill

    design-system

    Audit, document, or extend your design system. Use when checking for naming inconsistencies or hardcoded values across components, writing documentation for a component's variants, states, and accessibility notes, or designing a new pattern that fits the existing system.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 2 are common, 2 are distinctive, and 1 is very specific to this project.

  • 2

    Common

    Most of the 16 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 16 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

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  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

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