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Systematic Debugging

Checked
jarmen423·vlatest·testing

Systematically identifies and isolates software bugs using structured analysis and automated reproduction techniques.

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 jarmen423/skills --skill systematic-debugging --full-depth -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

jarmen423

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 jarmen423/skills at d016f7d2c7.

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

Scanned
11 files, 1,218 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 enables AI agents to systematically debug code, identifying and resolving errors through a structured process. It helps pinpoint root causes by analyzing error messages, suggesting potential fixes, and verifying solutions with targeted tests. The agent can also explain the debugging steps taken for better understanding and reproducibility.

When to use it

  • When an AI agent is generating incorrect or unexpected code output.
  • To troubleshoot errors encountered during automated testing processes.
  • For identifying performance bottlenecks in existing codebases.
  • When needing assistance in understanding complex error messages and their implications.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Suggestion of potential fixes
  • Verification with targeted tests
  • Explanation of debugging steps

Example prompts

  • "Debug the following Python code: [code snippet] - I'm getting a TypeError."
  • "I’m seeing slow performance in this function. Can you systematically debug it and suggest optimizations?"
  • "Explain how you would approach debugging this error message: 'IndexError: list index out of range'."

Tips & gotchas

The agent performs best when provided with clear error messages and the relevant code context. It may require additional information or clarification to fully understand complex issues.