debugging

Debug Python code by reproducing issues, analyzing logs, and verifying fixes.

Updated Dec 1, 2025

Search Tags:#debugging #troubleshooting #logs #python #stack-trace #reproduce-issues

Ready to run

npx skills add https://github.com/mandersogit/brynhild-harness --skill debugging-mandersogit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debugging
Source: https://github.com/mandersogit/brynhild-harness/tree/main/tests/fixtures/plugins/test-complete/skills/debugging
Command: npx skills add https://github.com/mandersogit/brynhild-harness --skill debugging-mandersogit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured approach to debugging Python code, helping you reproduce issues, gather context, form hypotheses, test solutions, and verify fixes, reducing time wasted on guesswork.

Core Features & Use Cases

Reproduce issues with exact error messages and minimal reproduction steps
Gather information: stack traces, logs, and environment details
Form and test hypotheses, add targeted logs or breakpoints, and validate fixes
Verify changes to prevent regressions in other parts of the codebase

Quick Start

Tell the agent about the error you’re seeing, e.g., "debugging: show me the stack trace for this exception" and provide any relevant code snippets or logs.

Frequently Asked Questions about debugging

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I debug Python errors using stack traces and logs?▼

Debugging Python errors starts with capturing the full stack trace and relevant logs to identify where the failure occurred. Gather the exact error message, trace the call sequence, and examine environment context to pinpoint the root cause before forming and testing hypotheses.

What's the best way to reproduce a Python error consistently?▼

Reproduce errors by isolating minimal steps that trigger the failure reliably. Create a small test case with the exact inputs and conditions, verify the error reappears, then use that reproduction to test fixes and confirm the issue is resolved without regression.

Can I use breakpoints and targeted logs to troubleshoot Python code?▼

Yes. Breakpoints and targeted logs are core debugging techniques for Python. Insert breakpoints at suspected failure points or add logging statements to inspect variable state, then step through execution or review logs to validate your hypothesis about what went wrong.

How do I verify that a Python fix doesn't break other tests?▼

After implementing a fix, run your test suite to verify the original error is resolved and no new failures appear elsewhere in the codebase. This regression testing ensures your solution is safe and doesn't introduce unexpected behavior in other parts of your application.

What information do I need to gather before debugging a Python issue?▼

Gather the exact error message, full stack trace, relevant code snippets, logs around the failure time, and environment details like Python version and dependencies. This context helps you form accurate hypotheses and test solutions efficiently rather than guessing.