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Systematic Debugging

Checked
brite-nites·vlatest·testing

Systematically identifies and resolves software bugs using AI analysis and targeted test case generation for faster debugging.

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 brite-nites/brite-claude-plugins --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

brite-nites

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 brite-nites/britenites-claude-plugins at fb1da06c47.

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

Commit
fb1da06c47
Manifest
plugins/workflows/skills/systematic-debugging/SKILL.md
Scanned
1 file, 204 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, Systematic Debugging, helps AI agents systematically identify and resolve errors in code or processes. It guides the agent through a structured debugging process, including isolating issues, formulating hypotheses, testing solutions, and documenting findings. The goal is to improve the efficiency and accuracy of problem-solving for AI agents.

When to use it

  • When an AI agent consistently produces incorrect outputs or unexpected behavior.
  • During development cycles to proactively identify and fix potential bugs in complex workflows.
  • To troubleshoot errors encountered by automated systems or scripts.
  • When debugging a process that is difficult to understand or trace due to its complexity.

Key capabilities

  • Structured Debugging Process: Guides the agent through steps like isolation, hypothesis generation, testing, and documentation.
  • Error Isolation: Helps pinpoint the specific source of an error.
  • Hypothesis Formulation: Facilitates generating potential explanations for observed behavior.
  • Solution Testing: Supports systematically evaluating proposed fixes.

Example prompts

  • "Debug this Python script; it's producing incorrect results."
  • "I’m seeing errors in my data processing pipeline, can you help me debug it?"
  • "Walk me through a systematic debugging process for this error message: [error message]"

Tips & gotchas

The agent will perform best when provided with clear and concise code or descriptions of the problematic process. Ensure sufficient context is given to allow the skill to effectively isolate and analyze the issue.