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Python Debugging

Checked
trotsky1997·vlatest·backend

Helps with Python, debugging as part of developing backend services and APIs 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 trotsky1997/my-claude-agent-skills --skill python-debugging -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

trotsky1997

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 trotsky1997/my-claude-agent-skills at 432d093a24.

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

Commit
432d093a24
Manifest
python-debugging/SKILL.md
Scanned
8 files, 1,749 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

This skill allows an AI agent to debug Python code. It can identify syntax errors, runtime exceptions, and logical flaws within provided Python scripts or snippets. The agent can then suggest corrections and improvements to ensure the code functions as intended.

When to use it

  • When encountering error messages while running a Python script.
  • To understand why a specific function isn't producing the expected output.
  • For identifying performance bottlenecks in existing Python code.
  • To assist with troubleshooting complex logic within a larger program.

Key capabilities

  • Syntax error detection and correction
  • Runtime exception analysis
  • Logical flaw identification
  • Code improvement suggestions

Example prompts

  • "Debug this Python code: [code snippet]"
  • "I'm getting an AttributeError in my Python script, can you help me find the issue? Here’s the traceback and relevant code."
  • "This Python function isn't returning the correct value. Can you review it for logical errors?"

Tips & gotchas

The agent requires access to the problematic Python code or a detailed description of the error encountered. Providing clear context, including tracebacks and expected behavior, will significantly improve debugging accuracy.