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Debug Like Expert

Checked
glittercowboy·vlatest·testing·MIT

Simulates expert debugging strategies, identifying root causes and suggesting fixes from test failures with detailed explanations.

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 glittercowboy/taches-cc-resources --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

glittercowboy

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 glittercowboy/taches-cc-resources at 1757615b99.

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

Commit
1757615b99
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 helps AI agents debug code by simulating expert debugging strategies. It encourages a skeptical approach to your own code and utilizes a scientific method to systematically identify root causes of test failures rather than applying quick fixes. The agent will scan the codebase for clues about the project type and, if possible, load relevant domain-specific expertise to aid in the investigation.

When to use it

  • When encountering unexpected test failures and needing help identifying the underlying cause.
  • For debugging projects where you suspect cognitive biases might be hindering your ability to spot errors.
  • When working with unfamiliar codebases and wanting a structured approach to problem-solving.
  • To leverage domain-specific knowledge for common pitfalls in specific project types (e.g., Python games, macOS apps).
  • When needing assistance debugging projects written in languages like Python, JavaScript, Rust, Swift, C++, Go, or Java.

Key capabilities

  • Systematic Debugging: Employs a scientific method to identify root causes.
  • Skeptical Code Review: Encourages questioning the assumptions behind code implementation.
  • Domain Expertise Loading: Can load domain-specific debugging knowledge from ~/.claude/skills/expertise/ (e.g., macos-apps, iphone-apps, python-games).
  • Automatic Domain Detection: Identifies project types based on file extensions and common configuration files (like package.json, Cargo.toml, setup.py).
  • Graceful Degradation: Functions effectively even without domain expertise loaded, using general debugging methodology.

Example prompts

  • "I'm seeing test failures in my Python game. Can you help me debug it?"
  • "My Rust project is crashing unexpectedly; can you investigate the root cause?"
  • "I have a failing test in my Next.js application, what could be causing it?"

Tips & gotchas

  • The skill relies on domain expertise files located in ~/.claude/skills/expertise/. Ensure these are present for optimal results when working with specific project types.
  • If no domain expertise is found or selected, the agent will fall back to general debugging techniques.
  • Be prepared for the agent to ask clarifying questions about your project type and codebase.