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Scientific Debugging

Checked
vidyfoo·vlatest·testing·MIT

Identifies and suggests fixes for errors in scientific code, datasets, and experimental workflows using AI analysis.

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 vidyfoo/antigravity-skill-engine --skill scientific-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

vidyfoo

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 vidyfoo/antigravity-skill-engine at 9502732b0d.

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

Commit
9502732b0d
Manifest
.agent/skills/2-audit/scientific-debugging/SKILL.md
Scanned
3 files, 102 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 allows an AI agent to systematically debug code or processes using a scientific method. It can formulate hypotheses about the cause of errors, design experiments to test those hypotheses, analyze results, and refine its understanding of the problem. The goal is to move beyond trial-and-error debugging towards a more structured and efficient troubleshooting process.

When to use it

  • Complex Code Errors: When faced with difficult-to-reproduce bugs or errors in software code that require methodical investigation.
  • Process Bottlenecks: To identify the root cause of inefficiencies or slowdowns within automated workflows or processes.
  • Unexpected System Behavior: When a system is behaving unexpectedly and requires a structured approach to diagnose the underlying issue.
  • Reproducing Issues: When trying to reproduce an error reported by a user, allowing for controlled experimentation.

Key capabilities

  • Hypothesis Generation: Formulates potential explanations for observed errors or unexpected behavior.
  • Experiment Design: Creates targeted tests to validate or refute hypotheses.
  • Result Analysis: Interprets the outcomes of experiments and draws conclusions.
  • Iterative Refinement: Adjusts hypotheses and experimental designs based on analysis results.

Example prompts

  • "I'm seeing intermittent errors in my data processing pipeline. Use scientific debugging to find the cause."
  • "This function is returning incorrect results. Can you use a scientific approach to debug it?"
  • "The application crashes when I perform action X. Investigate using hypothesis generation and testing."

Tips & gotchas

  • Requires clear error descriptions or observable behavior for effective hypothesis formulation.
  • May require access to logs, code, or system configurations depending on the scope of debugging needed.