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Systematic Debugging

Checked
jtsang4·vlatest·testing·MIT

Systematically identifies and resolves software bugs through automated analysis, reproduction, and targeted fix suggestions.

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 jtsang4/efficient-coding --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

jtsang4

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 jtsang4/efficient-coding at 054ee668f9.

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

Commit
054ee668f9
Manifest
skills/systematic-debugging/SKILL.md
Scanned
11 files, 1,220 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 systematically debug code. It can analyze error messages, identify potential root causes based on context, and suggest targeted fixes or debugging steps. The skill aims to improve efficiency in identifying and resolving software defects.

When to use it

  • When encountering unexpected behavior or errors in a codebase.
  • To troubleshoot issues reported by users or automated tests.
  • During code reviews to proactively identify potential bugs.
  • For complex debugging scenarios where traditional methods are insufficient.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Debugging step suggestion
  • Contextual understanding of code

Example prompts

  • "I'm getting a 'TypeError: undefined is not an object' error in my JavaScript code. Can you help me debug it?"
  • "My Python script is crashing with a segmentation fault. What could be the cause and how should I investigate?"
  • “The unit tests are failing; can you suggest debugging steps to isolate the problem?”

Tips & gotchas

This skill works best when provided with relevant code snippets or error logs for context. It's most effective on well-structured codebases where errors are clearly traceable.