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Systematic Debugging

Checked
eyadsibai·vlatest·testing

Systematically identifies and resolves software bugs using automated analysis and targeted test case generation.

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 eyadsibai/ltk --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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-engineering/skills/systematic-debugging/SKILL.md
Scanned
1 file, 236 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 provides a structured approach to debugging code, focusing on identifying root causes and suggesting targeted fixes. It systematically analyzes error messages, stack traces, and code context to pinpoint issues efficiently. The tool aims to reduce the time spent troubleshooting by guiding the agent through a logical debugging process.

When to use it

  • When an AI agent encounters unexpected errors during task execution.
  • To diagnose performance bottlenecks in existing code.
  • During development cycles to proactively identify and resolve potential issues.
  • When needing assistance understanding complex error messages or stack traces.

Key capabilities

  • Error message analysis
  • Stack trace interpretation
  • Code context examination
  • Root cause identification
  • Targeted fix suggestions

Example prompts

  • "Analyze this error message: 'TypeError: Cannot read property 'name' of undefined'"
  • "Explain the stack trace and suggest possible causes for this crash."
  • "I'm seeing slow performance in this function. Can you help me debug it?"

Tips & gotchas

The skill is most effective when provided with complete error messages, relevant code snippets, and clear descriptions of the observed behavior. It assumes a basic understanding of programming concepts to interpret its suggestions effectively.