Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计通过

litestar-logging莱特星日志记录

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

494

周安装

21

GitHub Stars

5

下载量

173
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:litestar-logging(莱特星日志记录)
来源仓库:https://github.com/alti3/litestar-skills
仓库路径:skills/litestar-logging
安装命令:
npx skills add https://github.com/alti3/litestar-skills --skill litestar-logging
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/alti3/litestar-skills --skill litestar-logging

简介

用于辅助测试设计、自动化测试、用例整理和回归验证。

  • 适合编写单元测试、端到端测试、测试计划或根据失败日志定位问题。
  • 使用时需确认项目测试框架、运行命令和夹具数据,避免误改真实逻辑。
  • 涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。
  • 安装方式:通过 GitHub 仓库添加,需确认权限与操作边界。

SKILL.md

Logging

Execution Workflow

  1. Choose the logging backend first: stdlib logging, picologging, Structlog, or a custom config subclass.
  2. Configure logging once at app setup with logging_config or the Structlog plugin.
  3. Use Litestar's non-blocking queue_listener handler unless a concrete reason requires otherwise.
  4. Decide when exceptions should be logged and which stack traces should be suppressed.
  5. Standardize request and app logger usage so fields, levels, and redaction rules stay consistent.
  6. Validate that logs remain actionable without leaking sensitive data.

Core Rules

  • Keep logging configuration centralized at app construction.
  • Prefer the built-in non-blocking queue_listener handler for async applications.
  • Treat exception logging policy as an explicit decision; Litestar does not log exceptions by default outside debug mode.
  • Use disable_stack_trace for expected exception types or status codes that should not spam traces.
  • Keep secrets, auth material, and sensitive request data out of logs.
  • Avoid duplicate logging across middleware, exception handlers, and business code.
  • Keep logging concerns separate from metrics and tracing.

Decision Guide

  • Use LoggingConfig for standard logging or picologging-based setups.
  • Use logging_module="picologging" when picologging is the desired backend.
  • Use StructlogPlugin when structured logging with Structlog is the project standard.
  • Use log_exceptions="always" when production incidents require exception logs even outside debug mode.
  • Use disable_stack_trace for common expected errors such as 404 or domain-level validation problems.
  • Subclass BaseLoggingConfig only when the built-in configs cannot express the needed behavior.

Reference Files

Read only the sections you need:

Recommended Defaults

  • Keep root level, handler choice, and formatter shape explicit.
  • Use request.logger for request-scoped logs and one shared app logger for app-level events.
  • Log exceptions intentionally instead of assuming Litestar will do it for you.
  • Suppress traces only for expected, high-volume failures.
  • Keep message fields and key names stable for searchability.

Anti-Patterns

  • Writing blocking log handlers into async request paths when queue_listener would suffice.
  • Logging secrets, tokens, or raw sensitive payloads.
  • Logging the same failure at multiple layers without adding new context.
  • Enabling stack traces for high-volume expected failures that operators already understand.
  • Subclassing logging config before exhausting built-in options.

Validation Checklist

  • Confirm logging is configured exactly once.
  • Confirm request and app loggers emit at the intended levels.
  • Confirm log_exceptions and disable_stack_trace match the incident policy.
  • Confirm secrets and sensitive request fields are absent or redacted.
  • Confirm queue-based handlers or equivalent non-blocking behavior are in place.
  • Confirm structured logging output matches downstream log ingestion expectations.

Cross-Skill Handoffs

  • Use litestar-metrics for quantitative observability and scrape/export concerns.
  • Use litestar-exception-handling for client-facing error contracts separate from logging behavior.
  • Use litestar-debugging for incident-driven troubleshooting workflows.
  • Use litestar-security when logging policy intersects with secrets, auth context, or redaction requirements.

Litestar References

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.74%
按下载量换算57

Claude

33.23%
按下载量换算57

Cursor

19.64%
按下载量换算34

Gemini CLI

9.16%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills