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Debugging Agent

Listed
psh355q-ui·vlatest·testing

Automatically identifies and suggests fixes for code errors based on error messages and stack traces.

Install on your platform

This skill no longer installs under the name debugging-agent. psh355q-ui/szdi57465yt is deleted or private, so it cannot be installed from.

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Repository install instructions

What we know about this skill

Publisher

psh355q-ui

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.

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Not scanned. repository not found or private Source recorded as psh355q-ui/szdi57465yt.
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About this skill

What it does

The debugging-agent skill allows you to diagnose and resolve issues within an AI agent's execution. It can identify errors in code, logic flaws impacting performance, and unexpected behavior by analyzing the agent’s steps and outputs. This skill provides a structured approach to pinpointing problems and suggesting corrective actions for improved agent reliability.

When to use it

  • Unexpected Agent Behavior: When an AI agent consistently produces incorrect or undesirable results.
  • Performance Bottlenecks: To identify slow execution times or resource constraints within the agent's workflow.
  • Error Handling: When encountering errors during agent operation and needing assistance in understanding their root cause.
  • Complex Agent Logic: When debugging intricate agent workflows with multiple steps and dependencies.

Key capabilities

  • Error analysis
  • Logic flaw detection
  • Performance bottleneck identification
  • Corrective action suggestions

Example prompts

  • "Analyze the last 5 steps of my agent and identify any potential errors."
  • "Why is this agent taking so long to complete? Show me a breakdown of execution times for each step."
  • "The agent returned an incorrect result. Can you help me understand why?"

Tips & gotchas

This skill requires access to the AI agent's execution logs and intermediate outputs for effective debugging. Providing detailed context about the agent’s purpose and expected behavior will improve the quality of suggested solutions.