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Systematic Debugging

Checked
lv416e·vlatest·testing

lv416e systematically identifies and isolates software bugs through methodical analysis and automated 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 lv416e/dotfiles --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

lv416e

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 lv416e/dotfiles at d182953f5e.

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

Commit
d182953f5e
Manifest
dot_claude/skills/systematic-debugging/SKILL.md
Scanned
6 files, 619 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 issues through a structured process. It facilitates pinpointing root causes by analyzing error messages, inspecting variable states, and suggesting potential fixes based on identified patterns. The agent can then apply these corrections and re-test the affected code segments.

When to use it

  • Troubleshooting failing automated tests: Use this skill when test suites are producing failures and you need a methodical approach to identify the source of the problem.
  • Debugging complex logic errors: Employ this skill when encountering unexpected behavior in software, requiring a step-by-step analysis of code execution.
  • Investigating production incidents: Leverage this skill to diagnose issues reported by users or detected through monitoring systems, ensuring efficient resolution and preventing recurrence.
  • Understanding unfamiliar codebase: When working with new or legacy code, use the skill to systematically explore its behavior and identify potential bugs.

Key capabilities

  • Error message analysis
  • Variable state inspection
  • Pattern-based fix suggestions
  • Code correction application
  • Re-testing of affected code segments

Example prompts

  • "Debug this Python script and explain the root cause of the error."
  • "Systematically debug this Java method, focusing on potential null pointer exceptions."
  • “Analyze these test failures and suggest a fix for the failing unit test.”

Tips & gotchas

The agent's effectiveness is dependent on access to relevant code files and debugging tools. Providing clear context about the problem being investigated will improve the quality of its suggestions.