skills/python-logging/SKILL.md
Use when choosing or configuring Python logging, especially deciding between stdlib logging and loguru for apps or CLIs.
npx skillsauth add narumiruna/agent-skills python-loggingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Choose the logging system based on project boundaries. Core principle: use stdlib logging for reusable libraries and ecosystem integration; use loguru only when an app or CLI owns the whole logging surface.
logging and loguru.| Need | Use |
| --- | --- |
| Library or long-lived service | stdlib logging |
| Simple app or CLI | loguru |
| Integrations (Sentry/OTel) | stdlib logging |
| Mixed library + app | stdlib in library; app config at boundary |
Use stdlib logging when:
Use loguru when:
logging.getLogger(__name__) and do not call basicConfig().Stdlib logger setup:
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.info("App started")
basicConfig() inside imported library modules.references/logging.md - stdlib logging patternsreferences/loguru.md - loguru patternsdevelopment
Score or compare one or more agent skills across trigger clarity, workflow actionability, safety boundaries, verification rigor, incremental knowledge value, and leanness. Use only when the user explicitly asks for ratings, numerical quality scores, rubric-based scorecards, or scored comparisons; use creating-agent-skills for unscored reviews or revisions.
development
Assess or improve an existing codebase's architecture when the user asks about module boundaries, coupling, scattered ownership, testability, change locality, deep modules, seams, or behavior-preserving structural refactoring. Use for cross-module design rather than ordinary diff review or a confirmed edge-case bug fix.
development
Perform read-only security audits, vulnerability assessments, or threat-focused reviews of diffs, pull requests, code paths, or explicitly scoped repositories when security is the primary objective or acceptance criterion. Use reviewing-code for ordinary review with baseline security coverage and hardening-code-paths for fixing confirmed findings.
development
Run iterative multi-reviewer panels over a code diff, verify their findings, apply explicitly authorized fixes, and re-review the updated change until it passes or reaches a stopping condition. Use when the user asks for a panel loop, multi-model code-review consensus, or a review-fix-re-review cycle.