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Systematic Debugging

Checked
dkyazzentwatwa·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 dkyazzentwatwa/supernavigator --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

dkyazzentwatwa

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 dkyazzentwatwa/supernavigator at a8851214e7.

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

Commit
a8851214e7
Manifest
superpowers-main/skills/systematic-debugging/SKILL.md
Scanned
22 files, 2,436 lines
Copies in repo
2 — all of them scanned
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, facilitates a structured approach to identifying and resolving issues within AI agent workflows. It helps pinpoint the root cause of problems through methodical testing and analysis. The tool provides a framework for isolating errors and ensuring reliable agent performance.

When to use it

  • When an AI agent is exhibiting unexpected or incorrect behavior.
  • During development and testing phases to proactively identify potential issues.
  • To efficiently debug complex workflows involving multiple steps or integrations.
  • After receiving user reports of inconsistent results from the agent.

Key capabilities

  • Systematic error isolation
  • Methodical testing framework
  • Root cause analysis
  • Workflow debugging

Example prompts

  • "Debug this workflow and identify why it's failing to complete."
  • "Run a systematic test of the data processing steps in this agent."
  • "Analyze the logs for errors and suggest potential fixes."

Tips & gotchas

The skill is most effective when provided with detailed information about the AI agent’s workflow, including relevant code or configuration files. Ensure sufficient logging is enabled within the agent to facilitate debugging.