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Systematic Debugging

Checked
shingle2302·vlatest·testing·MIT

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

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 shingle2302/superpowers --skill systematic-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

shingle2302

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 shingle2302/superpowers at a98c5dfc9d.

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

Commit
a98c5dfc9d
Manifest
skills/systematic-debugging/SKILL.md
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. It helps identify and isolate errors through a structured process, suggesting potential fixes based on error messages and code context. The agent can analyze stack traces, pinpoint problematic lines of code, and propose solutions for common programming mistakes.

When to use it

  • When an AI agent is generating code that produces runtime errors or unexpected behavior.
  • To troubleshoot complex bugs where the root cause isn't immediately obvious.
  • For debugging tasks involving unfamiliar codebases or libraries.
  • During automated testing processes to identify and resolve failing test cases.

Key capabilities

  • Error message analysis
  • Stack trace interpretation
  • Code context examination
  • Suggestion of potential fixes

Example prompts

  • "Debug this Python code: [code snippet] - I'm getting a TypeError."
  • "Analyze the stack trace and suggest what might be causing this error: [stack trace]"
  • "I have a failing test case. Can you help me debug the relevant code?"

Tips & gotchas

The agent’s effectiveness depends on providing clear error messages and sufficient code context. For complex debugging scenarios, breaking down the problem into smaller steps can improve results.