Does this skill require external dependencies?

While it recommends modern tools like Loguru and platformdirs for the best experience, it also provides patterns for the Python standard library's RotatingFileHandler for dependency-free environments.

Is it compatible with Windows, macOS, and Linux?

Yes, it utilizes the platformdirs library to ensure logs are automatically stored in the correct OS-specific directories, such as XDG paths on Linux or Library/Logs on macOS.

Why use JSONL instead of standard .log files?

JSONL (JSON Lines) provides a machine-readable format where each line is a valid JSON object, making it significantly easier for Claude Code and log aggregation tools to parse, filter, and analyze log data.

How does it handle log file growth?

The skill mandates log rotation patterns, allowing you to configure maximum file sizes and retention periods to prevent logs from consuming unbounded disk space.

Python Logging Best Practices

Name: Python Logging Best Practices
Author: terrylica

byterrylica

•

分析与监控

Implements standardized, machine-readable Python logging patterns for improved observability and AI-driven analysis.

This skill provides a unified reference and implementation framework for Python logging, ensuring logs are optimized for both human readability and machine analysis via Claude Code. By standardizing on JSONL (NDJSON) formats, it enables seamless log parsing and debugging. The skill emphasizes operational reliability through mandatory log rotation, retention policies, and cross-platform directory management using platformdirs, making it ideal for developers building CLI tools, daemons, or microservices that require production-grade telemetry.

主要功能

01Automated log rotation, retention, and compression patterns

02Standardized JSONL (NDJSON) output for machine readability

03Structured semantic fields including trace_id, metrics, and context

04Cross-platform log directory handling via platformdirs

05Support for both modern Loguru and standard library RotatingFileHandler

068 GitHub stars

使用场景

01Standardizing telemetry across microservices for easier log aggregation and monitoring

02Developing production-grade Python daemons with automated log maintenance

03Building CLI tools that provide structured logs for AI-assisted debugging

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add terrylica/cc-skills python-logging-best-practices

For use in Claude.ai and ChatGPT

Download Skill