python-debugpy

Debug Python applications using pdb and debugpy workflows.

Updated Feb 21, 2026

Search Tags:#debugging #troubleshooting #python #breakpoints #dap #pdb #debugpy

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npx skills add https://github.com/Gitnapp/Skills --skill python-debugpy-gitnapp

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Skill: python-debugpy
Source: https://github.com/Gitnapp/Skills/tree/main/software-development/python-debugpy
Command: npx skills add https://github.com/Gitnapp/Skills --skill python-debugpy-gitnapp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers diagnose Python bugs by providing practical workflows for interactive debugging, remote inspection, and post-mortem analysis when errors are difficult to reproduce.

Core Features & Use Cases

Interactive Debugging: Use pdb workflows with breakpoints, stepping, stack inspection, and runtime expression evaluation.
Remote Debugging: Attach debugpy through the Debug Adapter Protocol for long-running, headless, or subprocess-based Python applications.
Use Case: Investigate failing tests, daemon issues, async handlers, subprocess failures, or production-like crashes by inspecting live state and call stacks.

Quick Start

Use the python-debugpy skill to debug my Python application and identify why the failing code path produces an incorrect result.

Frequently Asked Questions about python-debugpy

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

FAQPage Schema

How do I debug a Python application that fails in a long-running service?▼

To debug a Python application with long-running services, you can attach debugpy through the Debug Adapter Protocol for remote inspection. This allows you to diagnose subprocess issues and headless runtime failures by inspecting live execution state.

When do I need to use post-mortem exception analysis in Python?▼

Post-mortem exception analysis is needed when Python bugs are difficult to reproduce. It inspects the execution state and call stacks at the moment of a crash, helping you diagnose production-like failures and unhandled exceptions after they occur.

Can I use pdb workflows to inspect async handlers and failing tests?▼

Yes, you can use pdb workflows to inspect async handlers and failing tests. Interactive debugging with pdb allows you to set breakpoints, step through code, inspect the stack, and evaluate runtime expressions to locate incorrect results.

Does debugpy support remote attachment for subprocess issues?▼

Yes, debugpy supports remote attachment through the Debug Adapter Protocol for subprocess issues. You can attach to long-running or headless Python applications to inspect live state, evaluate expressions, and diagnose runtime failures.

What is the best way to manage breakpoints and inspect runtime state in Python?▼

The best way to manage breakpoints and inspect runtime state is by using pdb and debugpy workflows for interactive debugging. These tools allow you to step through execution, inspect call stacks, and evaluate expressions to diagnose failing code paths.

Why does my Python application crash in production-like environments without clear errors?▼

Python applications may crash without clear errors due to complex runtime failures. You can diagnose these by attaching debugpy for remote inspection or performing post-mortem exception analysis to inspect live state and call stacks at the time of failure.