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Systematic Debugging

Checked
tai-ch0802·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 tai-ch0802/skills-bundle --skill systematic-debugging --full-depth -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

tai-ch0802

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 tai-ch0802/skills-bundle at 901ea3ae6e.

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

Commit
901ea3ae6e
Manifest
i18n/zh-TW/systematic-debugging/SKILL.md
Scanned
1 file, 98 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 enables AI agents to systematically debug code, identifying and resolving errors through a structured process. It facilitates pinpointing root causes of issues by analyzing error messages, tracing execution flow, and suggesting potential fixes. The agent can then apply these solutions and re-test for verification.

When to use it

  • Troubleshooting application crashes: When an application unexpectedly terminates with error messages.
  • Debugging failing unit tests: To diagnose why specific test cases are not passing.
  • Identifying performance bottlenecks: To analyze code execution and locate areas causing slowdowns.
  • Resolving logic errors: When the application produces incorrect results despite running without crashes.

Key capabilities

  • Error message analysis
  • Execution flow tracing
  • Root cause identification
  • Solution suggestion
  • Re-testing verification

Example prompts

  • "Debug this code and explain why it's failing: [code snippet]"
  • "I’m getting a NullPointerException in line 42. Can you help me find the source of the error?"
  • “This function is taking too long to execute. Can you identify any performance bottlenecks?”

Tips & gotchas

The agent's effectiveness depends on providing clear and concise code snippets or detailed descriptions of the problem encountered. Ensure sufficient context is provided for accurate debugging.