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Agent Skill · 研究检索

logging(logging 开发)

logging 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

logging 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 67,168。

总安装 250周安装 10GitHub Stars 9下载 81核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:logging(logging 开发)
来源仓库:https://github.com/florianbuetow/claude-code
仓库路径:skills/logging
安装命令:
npx skills add https://github.com/florianbuetow/claude-code --skill 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/florianbuetow/claude-code --skill logging

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。logging 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词快速定位候选结果。可结合来源仓库继续核验具体用法。安装前建议确认权限和维护状态。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

注意是否会触发联网或文件读写。

来源仓库内路径:skills/logging

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/florianbuetow/claude-code --skill logging;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

logging 怎么安装?

在终端执行 npx skills add https://github.com/florianbuetow/claude-code --skill logging。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

logging 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

logging 需要什么权限?

权限级别是需要联网。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。

logging 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

logging 用的人多吗?

公开数据是累计安装 250 次,近一周安装 10 次,下载 81 次,GitHub 9 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

logging 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
logging

logging 开发 · 本页

2509需要联网未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Security Logging and Monitoring Failures (A09:2021)

Analyze source code for security logging and monitoring failures including missing audit logging for security events, sensitive data in logs, log injection, absence of alerting on failures, logs only stored locally, and missing tamper protection.

This is the most architectural OWASP category. Scanners provide minimal coverage for logging failures, so Claude's analysis of code patterns, logging configuration, and event coverage is the primary value of this skill.

Supported Flags

Read ../../shared/schemas/flags.md for the full flag specification. This skill supports all cross-cutting flags. Key flags for this skill:

  • --scope determines which files to analyze (default: changed)
  • --depth standard reads code and checks logging around security-critical operations
  • --depth deep traces security event flows to verify each produces an audit log entry
  • --severity filters output (logging gaps are often medium, sensitive data in logs is high)

Framework Context

Read ../../shared/frameworks/owasp-top10-2021.md, section A09:2021 - Security Logging and Monitoring Failures, for the full category description, common vulnerabilities, and prevention guidance.

Key CWEs in scope:

  • CWE-117: Improper Output Neutralization for Logs (log injection)
  • CWE-223: Omission of Security-Relevant Information
  • CWE-532: Insertion of Sensitive Information into Log File
  • CWE-778: Insufficient Logging
  • CWE-779: Logging of Excessive Data

Detection Patterns

Read references/detection-patterns.md for the full catalog of code patterns, search heuristics, language-specific examples, and false positive guidance.

Workflow

1. Determine Scope

Parse flags and resolve the file list per ../../shared/schemas/flags.md. Filter to files likely to contain logging logic or security-critical operations:

  • Authentication modules (**/auth/**, **/login/**, **/session/**)
  • Access control and authorization (**/middleware/**, **/guards/**, **/policies/**)
  • Logging configuration (**/logging/**, **/logger/**, **/*log*config*)
  • Error handlers (**/errors/**, **/exceptions/**, **/handlers/**)
  • Route/controller definitions (**/routes/**, **/controllers/**, **/api/**)
  • Configuration files (**/config/**, *.yaml, *.toml, *.ini, *.env*)

2. Check for Available Scanners

Detect scanners per ../../shared/schemas/scanners.md:

  1. semgrep -- can detect some log injection and sensitive data in logs
  2. bandit -- Python-specific logging issues (e.g., sensitive data in debug logs)

Record which scanners are available and which are missing. Note: scanner coverage for logging failures is limited. Claude analysis is the primary detection mechanism for this category.

3. Run Scanners (If Available)

If semgrep is available, run with rules targeting logging patterns:

semgrep scan --config auto --json --quiet <target>

Filter results to rules matching log injection, sensitive data exposure in logs, and logging configuration issues. Normalize output to the findings schema.

4. Claude Code Analysis

This is the primary analysis step for logging failures. Perform manual code analysis:

  1. Authentication event logging: Find login, logout, failed login, password reset, and MFA flows. Verify each produces an audit log entry with user identity, timestamp, IP address, and outcome (success/failure).
  2. Access control failure logging: Find authorization checks and verify that denied access attempts are logged with sufficient detail for investigation.
  3. Sensitive data in logs: Grep for log statements and check that passwords, tokens, API keys, credit card numbers, SSNs, and other PII are not logged.
  4. Log injection: Find log statements that include user-controlled input and verify the input is sanitized or the logging framework handles neutralization.
  5. Error handling: Find catch/except blocks and verify they log the error rather than swallowing it silently.
  6. Logging configuration: Check for centralized logging setup, structured log format, log level configuration, and whether logs are sent to a remote destination.
  7. Tamper protection: For high-value audit trails, check for integrity controls (append-only storage, checksums, write-once destinations).

When --depth deep, additionally trace:

  • Complete authentication flow from entry to audit log
  • Exception propagation chains to verify no errors are silently dropped
  • Logging pipeline from application code to destination (local file, remote service, SIEM)

5. Report Findings

Format output per ../../shared/schemas/findings.md using the LOG prefix (e.g., LOG-001, LOG-002).

Include for each finding:

  • Severity and confidence
  • Exact file location with code snippet
  • Impact description specific to the logging failure
  • Concrete fix with diff when possible
  • CWE and OWASP references

What to Look For

These are the high-signal patterns specific to logging and monitoring failures. Each maps to a detection pattern in references/detection-patterns.md.

  1. Missing authentication event logging -- Login, failed login, logout, password reset, and MFA events that produce no audit log entry.
  2. Sensitive data in log statements -- Passwords, tokens, API keys, credit card numbers, or PII written to logs, especially at DEBUG or INFO level.
  3. Log injection via user input -- User-controlled strings passed directly into log format strings without sanitization, enabling log forgery or CRLF injection.
  4. Missing access control failure logging -- Authorization denials that are not logged, making it impossible to detect brute-force or enumeration attacks.
  5. Silent error swallowing -- Catch/except blocks with pass, empty bodies, or comments like "ignore" that discard errors without logging.
  6. No centralized logging configuration -- Logging set up ad-hoc per file with no consistent format, level, or destination configuration.
  7. Logs stored only locally -- Log output goes to local files or stdout with no forwarding to a centralized log management system.
  8. Missing alerting configuration -- No evidence of alerting thresholds for security-critical events (repeated failed logins, privilege escalation attempts).
  9. Excessive logging of request/response bodies -- Logging full HTTP bodies that may contain sensitive data without redaction.

Scanner Integration

ScannerCoverageCommand
semgrepLog injection, sensitive data in debug logssemgrep scan --config auto --json --quiet <target>
banditPython logging of sensitive databandit -r <target> -f json -q

Fallback (no scanner): Use Grep with patterns from references/detection-patterns.md to find log statements with user input interpolation, catch blocks without logging, authentication functions without log calls, and sensitive data patterns in log arguments. Report findings with confidence: medium.

Scanner coverage for this category is inherently limited. Most logging failures are architectural gaps (missing logging) rather than code-level bugs (present but incorrect code), making Claude analysis the primary detection mechanism.

Relevant semgrep rule categories:

  • python.lang.security.audit.logging.*
  • javascript.express.security.audit.logging.*
  • java.lang.security.audit.logging.*
  • generic.logging.security.*

Output Format

Use the findings schema from ../../shared/schemas/findings.md.

  • ID prefix: LOG (e.g., LOG-001)
  • metadata.tool: logging
  • metadata.framework: owasp
  • metadata.category: A09
  • references.owasp: A09:2021
  • references.stride: R (Repudiation)

Severity guidance for this category:

  • critical: Sensitive data (passwords, tokens) logged in plaintext in production
  • high: No audit logging for authentication events, log injection enabling log forgery
  • medium: Silent error swallowing in security-critical paths, logs only stored locally
  • low: Inconsistent log format, missing structured logging, minor gaps in non-critical logging

平台分布

Codex

34.44%
按下载量换算28

Claude

29.83%
按下载量换算24

Cursor

17.73%
按下载量换算14

Gemini CLI

9%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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