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Python

Checked
pproenca·vlatest·backend·MIT

Automates Python backend tasks like data processing, API integration, and script execution via configurable workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pproenca/dot-skills --skill python -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

pproenca

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned pproenca/dot-skills at cf93c57cac.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
cf93c57cac
Manifest
skills/.curated/python/SKILL.md
Scanned
47 files, 2,183 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The Python skill enables AI agents to execute code, perform data analysis, and automate complex backend tasks using the Python programming language. It provides a robust environment for running scripts directly within the agent's workflow.

When to use it

  • Automating repetitive data processing or file manipulation tasks.
  • Performing quick calculations, statistical analysis, or mathematical modeling.
  • Generating dynamic reports or visualizations from raw datasets.
  • Prototyping backend logic before integrating it into a larger application.

Key capabilities

  • Executing Python scripts directly within the agent context.
  • Leveraging standard libraries and external packages for extended functionality.
  • Processing structured data formats like CSV, JSON, and XML.
  • Generating code snippets or full scripts based on natural language requests.

Example prompts

  • "Analyze this CSV file containing sales data and calculate the total revenue per region."
  • "Write a Python script to scrape specific URLs and save the text content to a local folder."
  • "Create a function that converts a list of dates into ISO format strings."

Tips & gotchas

Ensure you have internet access enabled if your workflow requires installing external packages via pip. Be mindful of execution time limits when running complex or memory-intensive scripts.