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Automated Instrumented Debugging

Checked
minleev5·vlatest·testing

Automatically instruments code, identifies bugs via runtime analysis, and generates debugging suggestions based on observed behavior.

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 minleev5/automated-instrumented-debugging-skill --skill automated-instrumented-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

minleev5

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 minleev5/automated-instrumented-debugging-skill at c4f0107359.

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

Commit
c4f0107359
Manifest
SKILL.md
Scanned
12 files, 965 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 automates the process of instrumented debugging. It allows AI agents to automatically add logging and instrumentation code to applications, then run tests and analyze the results to identify and diagnose issues. The agent can pinpoint root causes by correlating logs with test failures, significantly reducing manual debugging effort. This capability facilitates faster iteration cycles and improved software quality.

When to use it

  • Debugging failing automated tests: Use this skill when your automated tests are failing and you need help identifying the source of the problem.
  • Performance bottlenecks: Identify slow sections of code by instrumenting for performance metrics and analyzing execution times.
  • Complex system issues: Troubleshoot problems in distributed systems where pinpointing the root cause is difficult due to multiple interacting components.
  • Reproducing intermittent bugs: Generate detailed logs during test runs to help reproduce rare or hard-to-diagnose errors.

Key capabilities

  • Automated instrumentation code insertion
  • Test execution with added logging
  • Log correlation with test failures
  • Root cause analysis based on log data

Example prompts

  • "Debug the failing payment_processing test and add detailed logs to the calculate_total function."
  • "Instrument the image_resize function for performance metrics and run a benchmark test."
  • “Analyze these test results and identify the root cause of the failure, using the generated log data.”

Tips & gotchas

The skill requires access to the application's source code and testing environment. Be mindful that excessive instrumentation can impact application performance during debugging; consider targeted instrumentation for specific areas of concern.