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Systematic Debugging

Checked
ericgrill·vlatest·testing·MIT

Systematically identifies and resolves software bugs using a structured debugging process guided by ericgrill's expertise.

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 ericgrill/agents-skills-plugins --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

ericgrill

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 ericgrill/agents-skills-plugins at 43a037fd79.

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

Commit
43a037fd79
Manifest
plugins/superpowers/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, systematic-debugging, helps AI agents systematically identify and resolve bugs in code. It facilitates a structured approach to debugging by guiding the agent through steps like isolating the problem, formulating hypotheses, testing those hypotheses, and verifying fixes. The goal is to improve the efficiency and accuracy of bug resolution within software development workflows.

When to use it

  • When an AI agent is responsible for maintaining or extending codebases and needs a structured debugging process.
  • During automated testing cycles where identifying root causes quickly is crucial.
  • To assist developers in complex debugging scenarios, providing a guided approach.
  • When dealing with regressions or unexpected behavior in existing software systems.

Key capabilities

  • Structured Debugging Process: Guides the agent through isolation, hypothesis generation, and verification steps.
  • Root Cause Analysis: Helps identify the underlying cause of bugs rather than just addressing symptoms.
  • Automated Testing Integration: Can be integrated with automated testing frameworks for efficient debugging cycles.

Example prompts

  • "Debug this Python code snippet and explain your reasoning step-by-step."
  • "I'm seeing a regression in the reporting module; can you systematically debug it?"
  • "This function is returning incorrect results. Walk me through how you would systematically find the bug."

Tips & gotchas

The skill’s effectiveness depends on providing clear and concise code snippets or descriptions of the problem to the AI agent. It's most useful when applied to well-defined, modular codebases.