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Kernel Python Sdk

Checked
kernel·vlatest·backend·MIT

Provides Python guidance and assistance for developing backend services and APIs.

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 kernel/skills --skill kernel-python-sdk -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

kernel

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 kernel/skills at 1dfb54bb63.

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

Commit
1dfb54bb63
Manifest
plugins/kernel-sdks/skills/kernel-python-sdk/SKILL.md
Scanned
2 files, 569 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 kernel-python-sdk provides a Python interface to interact with Kernel, enabling AI agents to execute code and manage computational environments directly. It allows agents to leverage backend processing power for data analysis, model training, and complex algorithmic tasks within a secure sandbox.

When to use it

  • Automating data preprocessing pipelines that require heavy numerical computation.
  • Iteratively testing and refining machine learning models without manual setup.
  • Executing custom scripts to generate dynamic reports or visualizations from live datasets.
  • Performing rapid prototyping of algorithms by leveraging pre-configured computational resources.

Key capabilities

  • Direct execution of Python code blocks within the Kernel environment.
  • Management of long-running computational tasks and stateful sessions.
  • Secure isolation for running untrusted or experimental code safely.
  • Integration with standard Python libraries for data science and engineering workflows.

Example prompts

  • "Use the kernel-python-sdk to load a CSV file, calculate the mean and median of the 'price' column, and display the results."
  • "Execute a Python script that trains a simple linear regression model on the provided dataset and outputs the R-squared score."
  • "Run a loop to generate Fibonacci numbers up to 1000 using the kernel-python-sdk and print the first ten values."

Tips & gotchas

Ensure your AI agent has explicit permissions to execute code, as running arbitrary scripts requires trust in the environment. Be mindful of execution timeouts; complex computations may need to be broken into smaller steps or optimized for performance within the sandbox limits.