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Python Dev

Checked
databricks-solutions·vlatest·backend

Automates Python development tasks like code generation, testing, and deployment within Databricks environments.

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 databricks-solutions/ai-dev-kit --skill python-dev -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

databricks-solutions

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 databricks-solutions/ai-dev-kit at b059fd017a.

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

Commit
b059fd017a
Manifest
.claude/skills/python-dev/SKILL.md
Scanned
1 file, 201 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

What it does

The python-dev skill enables AI agents to install, configure, and manage Python development environments directly within their execution context. It streamlines the setup of local or containerized Python runtimes required for executing complex data engineering and machine learning tasks.

When to use it

  • You need to execute custom Python scripts that rely on specific libraries not available in the base agent environment.
  • You are building a data pipeline that requires native Python execution for heavy data transformation logic.
  • Your workflow involves training or fine-tuning models where the agent must manage dependencies dynamically.
  • You require a consistent Python runtime version across different deployment stages without manual setup.

Key capabilities

  • Installs and configures Python interpreters on demand.
  • Manages virtual environments to isolate project dependencies.
  • Executes arbitrary Python code blocks within the agent session.
  • Handles package installation via standard tools like pip or conda.

Example prompts

  • "Install scikit-learn and pandas, then write a script to load this CSV file and calculate column means."
  • "Set up a virtual environment with Python 3.9 and run a Jupyter notebook cell containing my data preprocessing logic."
  • "Configure the agent to use a specific version of PyTorch and execute a training loop on the provided dataset."

Tips & gotchas

Ensure your prompts explicitly specify package versions if reproducibility is critical, as default installations may vary. Be mindful that installing heavy libraries can increase execution time and resource consumption within the agent's sandbox.