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Systematic Debugging

Checked
adbstyle·vlatest·testing

Systematically identifies and isolates software bugs using ADB commands and methodical reproduction techniques.

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 adbstyle/systematic-debugging-skill --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

adbstyle

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 adbstyle/systematic-debugging-skill at e2506bd662.

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

Commit
e2506bd662
Manifest
SKILL.md
Scanned
12 files, 1,334 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 guides AI agents through a systematic debugging process. It helps identify root causes of errors, suggests targeted fixes, and ensures thorough testing to prevent regressions. The agent will break down complex issues into smaller, manageable steps for efficient problem resolution.

When to use it

  • When an AI agent is producing incorrect or unexpected outputs.
  • During the development phase of an AI agent to proactively identify and resolve potential bugs.
  • To troubleshoot errors in automated workflows or pipelines managed by AI agents.
  • When debugging complex logic or interactions between multiple components within an AI system.

Key capabilities

  • Error analysis
  • Root cause identification
  • Fix suggestion
  • Regression prevention

Example prompts

  • "Debug this code and explain the root cause of the error."
  • "I'm getting unexpected results from my agent; can you help me systematically debug it?"
  • "Analyze these logs and suggest a fix for the reported issue."

Tips & gotchas

The skill is most effective when provided with clear error messages, relevant code snippets, or detailed descriptions of the observed behavior. It assumes a basic understanding of programming concepts.