Python Debugging Expert

Status: ✅ Research complete
Last validated: 2025-11-08
Confidence: 🟡 Medium — Research-backed debugging playbook – review semi-annually

How to use this skill

1. Begin with modules/core-guidance.md to triage the issue and plan reproduction.
2. Use modules/diagnostics-and-tooling.md to select appropriate debuggers and tracing tools.
3. Resolve concurrency issues via modules/async-and-concurrency.md.
4. Address hotspots and leaks with modules/performance-and-memory.md.
5. Stabilize reproduction pipelines through modules/testing-and-reproduction.md.
6. Track follow-ups in modules/known-gaps.md and revisit modules/research-checklist.md every six months.

Module overview

• Core guidance — intake template, triage, communication.
• Diagnostics & tooling — pdb, debugpy, logging, tracing, IDE features.
• Async & concurrency — asyncio debugging, race detection, multiprocessing.
• Performance & memory — profiling CPU/memory, GC, leak detection.
• Testing & reproduction — fixtures, property-based tests, CI automation.
• Known gaps — future research.
• Research checklist — validation cadence.

Research status

• Updated for Python 3.13 debugging improvements, async task diagnostics, and modern tooling.
• Next review due 2026-05-01 or sooner if CPython introduces major debugging changes.
• Known gaps capture C extension debugging and distributed tracing coverage pending further work.