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Notebook Debugger

Checked
dangeles·vlatest·testing

This Notebook Debugger helps identify and fix errors in your Jupyter notebooks by stepping through code execution and inspecting variables – boosting debugging efficiency.

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 dangeles/claude --skill notebook-debugger -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

dangeles

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 dangeles/claude at f06426b141.

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

Commit
f06426b141
Manifest
claude-config/skills/notebook-debugger/SKILL.md
Scanned
7 files, 3,274 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 notebook-debugger skill allows AI agents to debug Python notebooks. It can identify and explain errors in code, suggest fixes, and step through execution to understand program flow. This enables the agent to troubleshoot issues within notebook environments more effectively.

When to use it

  • When an AI agent is generating or modifying Python notebooks and encounters runtime errors.
  • To help an agent understand why a particular cell in a notebook isn’t producing the expected output.
  • For agents that need to diagnose performance bottlenecks within a notebook's code execution.
  • When you want an agent to explain complex notebook logic step-by-step.

Key capabilities

  • Error identification and explanation
  • Code suggestion for error resolution
  • Step-through execution debugging
  • Explanation of program flow

Example prompts

  • "Debug this notebook cell: [paste code]"
  • "Explain why I'm getting a TypeError in this notebook."
  • "Can you step through the execution of this notebook and tell me what each cell does?"

Tips & gotchas

The agent needs access to the relevant notebook environment to effectively debug. Ensure that necessary libraries are installed within that environment for accurate error identification and code suggestions.