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Discover Debugging

Checked
rand·vlatest·testing·MIT

Automatically identifies potential bugs and suggests fixes in code based on test failures and error messages.

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 rand/cc-polymath --skill discover-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

rand

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 rand/cc-polymath at baa2df1144.

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

Commit
baa2df1144
Manifest
skills/discover-debugging/SKILL.md
Scanned
1 file, 77 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 discover-debugging skill helps AI agents identify and resolve issues within code or processes. It allows users to install skills, suggesting potential solutions based on identified problems. This streamlines troubleshooting by providing readily available debugging tools and expertise directly within the agent's workflow.

When to use it

  • When an AI agent is encountering errors during task execution and requires assistance in identifying the root cause.
  • To quickly integrate specialized debugging skills into an agent’s capabilities without manual coding or configuration.
  • For automating the process of finding and applying fixes for common programming or operational issues.
  • During development cycles to rapidly test and debug new features within AI agents.

Key capabilities

  • Skill installation
  • Problem identification
  • Solution suggestion

Example prompts

  • "Install a skill to help me debug this Python script."
  • "I'm getting an error message 'TypeError: unsupported operand type(s) for +'. Can you suggest a debugging skill?"
  • "Find a skill that can identify memory leaks in my application."

Tips & gotchas

The effectiveness of the suggested solutions depends on the clarity and specificity of the problem description provided to the AI agent. Ensure sufficient context is given when requesting assistance with debugging.