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Systematic Debugging

Checked
cygnusfear·vlatest·testing

Systematically identifies and resolves software bugs using advanced analysis and automated debugging 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 cygnusfear/claude-stuff --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

cygnusfear

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 cygnusfear/claude-stuff at 32efa59eb5.

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

Commit
32efa59eb5
Manifest
skills/systematic-debugging/SKILL.md
Scanned
11 files, 1,227 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, cygnusfear-systematic-debugging, assists in identifying and resolving issues within AI agent workflows. It systematically analyzes error messages to pinpoint root causes and suggests potential fixes for common problems. The tool aims to improve debugging efficiency by providing structured insights into failures.

When to use it

  • When an AI agent consistently fails a specific task or produces unexpected results.
  • To quickly understand the underlying reason behind a cryptic error message.
  • During development and testing phases to proactively identify and address potential issues.
  • When troubleshooting complex workflows involving multiple steps or dependencies.

Key capabilities

  • Error message analysis
  • Root cause identification
  • Suggestion of potential fixes
  • Systematic workflow debugging

Example prompts

  • "Analyze this error message: 'TypeError: Cannot read property 'name' of undefined'"
  • "I'm getting a 404 error when calling the API. Can you help me debug?"
  • "My agent is failing to complete task X. What are some possible causes?"

Tips & gotchas

The skill’s effectiveness depends on providing clear and detailed error messages for analysis. It may not be suitable for debugging issues outside of typical AI agent workflow failures.