How does this skill improve application performance?

It provides timing decorators and context manager timers to measure execution duration, enabling developers to find and fix slow code blocks effectively.

Does it support interactive debugging during development?

Absolutely. It contains a comprehensive cheat sheet for pdb and ipdb commands, as well as strategies for post-mortem debugging after a crash.

Are there best practices for error isolation?

Yes, the skill covers tactical debugging strategies like Binary Search Debugging and Rubber Duck Debugging to help isolate issues in complex codebases.

Can this skill help with structured logging?

Yes, it provides implementation patterns for JSON-formatted logs, including metadata context and appropriate log-level management for production environments.

What Python profiling tools does this skill support?

The skill includes guidance for cProfile (CPU profiling), line_profiler (line-by-line analysis), memory_profiler (memory usage), and py-spy (sampling profiling).

Python Observability & Debugging

Name: Python Observability & Debugging
Author: akaszubski

byakaszubski

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분석 및 모니터링

Implements structured logging, interactive debugging, and comprehensive performance profiling for Python applications.

소개

This skill equips Claude with advanced diagnostic capabilities for Python development, focusing on structured logging, performance profiling, and interactive debugging techniques. It provides standardized patterns for JSON-based logging, memory and CPU usage analysis using tools like cProfile and line_profiler, and tactical debugging strategies using pdb/ipdb. By following these production-ready patterns, developers can identify bottlenecks, resolve complex bugs, and monitor application health with machine-readable diagnostics and rich context.

주요 기능

Structured JSON logging for machine-readable and searchable diagnostics
Performance monitoring via timing decorators and custom metrics
10 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Interactive debugging patterns using pdb, ipdb, and post-mortem analysis
Comprehensive guide to debugging strategies and logging anti-patterns

사용 사례

Identifying and fixing performance bottlenecks in data-heavy applications
Diagnosing intermittent production bugs using structured log context
Standardizing logging and monitoring across multiple microservices

소개

주요 기능

Structured JSON logging for machine-readable and searchable diagnostics
Performance monitoring via timing decorators and custom metrics
10 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Interactive debugging patterns using pdb, ipdb, and post-mortem analysis
Comprehensive guide to debugging strategies and logging anti-patterns

사용 사례

Identifying and fixing performance bottlenecks in data-heavy applications
Diagnosing intermittent production bugs using structured log context
Standardizing logging and monitoring across multiple microservices