Skip to content
TrustedSkillsbeta
Back to all skills

Debug Loop

Checked
proffesor-for-testing·vlatest·testing·MIT

Automatically identifies and iteratively fixes test failures by analyzing root causes and suggesting code adjustments.

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 proffesor-for-testing/agentic-qe --skill debug-loop -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

proffesor-for-testing

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 proffesor-for-testing/agentic-qe at 124d8c735e.

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

Commit
124d8c735e
Manifest
assets/skills/debug-loop/SKILL.md
Scanned
2 files, 120 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

The debug-loop skill allows an AI agent to step through a process or set of instructions, providing detailed output at each stage. This is particularly useful for identifying errors and bottlenecks within complex workflows. It helps agents systematically analyze their actions and the results they produce, facilitating iterative refinement and improved performance.

When to use it

  • Troubleshooting failed tasks: If an agent consistently fails to complete a task, use debug-loop to understand where the process is breaking down.
  • Analyzing complex workflows: Break down multi-step processes into smaller, manageable chunks for detailed inspection and optimization.
  • Understanding decision-making: Gain insight into why an agent made specific choices by observing its reasoning at each step of a task.
  • Improving agent efficiency: Identify slow or redundant steps within a process to streamline the agent's actions.

Key capabilities

  • Step-by-step execution with detailed output
  • Process breakdown for error identification
  • Workflow analysis and optimization
  • Decision-making transparency

Example prompts

  • "Run this script in debug mode, showing me each step."
  • "Debug the following workflow: [workflow description]."
  • "Explain your reasoning at each stage of completing this task."

Tips & gotchas

The skill’s effectiveness relies on clear and well-defined instructions for the agent to follow. Complex or ambiguous tasks may produce overwhelming debug output, so start with simpler scenarios.