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Debug Like Expert

Checked
cfircoo·vlatest·testing·MIT

Analyzes code errors, suggests fixes, and explains reasoning like a seasoned debugging expert for thorough testing.

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 cfircoo/claude-code-toolkit --skill debug-like-expert -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

cfircoo

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 cfircoo/claude-code-toolkit at d6264305dc.

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

Commit
d6264305dc
Manifest
skills/debug-like-expert/SKILL.md
Scanned
6 files, 1,998 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 debug code like an experienced developer. It can analyze error messages, identify root causes of bugs, and suggest fixes based on best practices. The tool provides detailed explanations for its reasoning, making it valuable for learning and understanding complex debugging processes.

When to use it

  • When encountering unfamiliar error messages or cryptic stack traces in a codebase.
  • To quickly diagnose performance bottlenecks within existing code.
  • For identifying the underlying cause of unexpected program behavior.
  • When needing assistance with debugging legacy codebases where documentation is scarce.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Fix suggestion generation
  • Reasoning explanation

Example prompts

  • "Analyze this error message: TypeError: Cannot read property 'name' of undefined and suggest a fix."
  • "I'm seeing slow performance in this function. Can you identify potential bottlenecks?"
  • "This code is producing unexpected results. Explain why and propose corrections."

Tips & gotchas

The skill’s effectiveness depends on the clarity and completeness of the provided error messages or code snippets. Providing context, such as relevant file paths or dependencies, can improve accuracy.