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Debug

Checked
zhiruifeng·vlatest·testing·MIT

Automatically identifies and suggests fixes for common coding errors in Python and JavaScript projects.

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 zhiruifeng/localagentcrew --skill debug -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

zhiruifeng

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 zhiruifeng/localagentcrew at 00a039e359.

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

Commit
00a039e359
Manifest
.claude/skills/debug/SKILL.md
Scanned
1 file, 86 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, zhiruifeng-debug, provides debugging capabilities for AI agents. It allows users to inspect and modify the agent's internal state during execution. This is useful for understanding why an agent is behaving in a certain way and correcting errors or improving performance.

When to use it

  • Troubleshooting unexpected behavior: If your agent isn’t performing as expected, use this skill to examine its thought process.
  • Understanding complex workflows: Debug the execution flow of multi-step tasks to identify bottlenecks or inefficiencies.
  • Validating assumptions: Verify that the agent is reasoning correctly and using information as intended.
  • Improving agent performance: Analyze internal states to optimize decision making and resource utilization.

Key capabilities

  • Inspect agent state during execution
  • Modify agent state for testing purposes
  • Provides insights into agent’s thought process

Example prompts

  • "Debug the last step of my workflow."
  • "Show me the current value of 'temperature'."
  • "What was the reasoning behind your last decision?"

Tips & gotchas

This skill requires a good understanding of the agent's internal workings. Incorrectly modifying state can lead to unpredictable behavior, so use with caution and in controlled testing environments.