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Qe Debug Loop

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

Automates Q&E debug loops by systematically exploring variations to isolate and resolve test failures quickly.

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 qe-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
.kiro/skills/qe-debug-loop/SKILL.md
Scanned
1 file, 59 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

The qe-debug-loop skill allows an AI agent to systematically debug and refine iterative processes, particularly those involving loops or repeated actions. It helps identify errors in logic, track variable states across iterations, and pinpoint inefficiencies within a sequence of steps. This results in more reliable and optimized automated workflows.

When to use it

  • Automated Testing: Debugging failing test scripts that execute multiple steps.
  • Data Processing Pipelines: Identifying bottlenecks or incorrect transformations within data processing loops.
  • Workflow Optimization: Analyzing a series of actions to find areas for improvement in efficiency and accuracy.
  • Complex Task Execution: Troubleshooting errors when an agent is performing a multi-step task with conditional logic.

Key capabilities

  • Iteration Tracking: Monitors variable values across loop iterations.
  • Error Logging: Records specific error messages and states during execution.
  • Conditional Breakpoints: Allows pausing at specific conditions within loops.
  • Step-by-Step Analysis: Provides detailed logs of each step taken in a process.

Example prompts

  • "Debug the following Python script, focusing on loop iteration 5:"
  • "Analyze this data processing pipeline and identify why the output is incorrect."
  • "Trace the execution of this workflow and highlight any potential bottlenecks."

Tips & gotchas

The skill requires access to the code or process being debugged. Ensure the agent has sufficient permissions and context to effectively analyze and report on the iterative steps.