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ln-627-observability-auditorln 627 可观察性审核员

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

6,830

周安装

279

GitHub Stars

437

下载量

2,187
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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

3

许可证

MIT

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:ln-627-observability-auditor(ln 627 可观察性审核员)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-627-observability-auditor
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-627-observability-auditor
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-627-observability-auditor

简介

用于安全审计、权限检查和常见漏洞排查,适合梳理敏感配置和鉴权逻辑。

  • 适用于需要分析凭据风险或生成安全复核清单的场景。
  • 使用时不能把工具输出直接当最终结论,需先确认最小权限和操作边界。
  • 涉及密钥、令牌或用户数据时应脱敏处理,避免影响生产系统。
  • 安装前建议确认权限范围和维护状态,确保不会触发不必要的网络或文件操作。

SKILL.md

Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If shared/ is missing, fetch files via WebFetch from https://raw.githubusercontent.com/levnikolaevich/claude-code-skills/master/skills/{path}.

Observability Auditor (L3 Worker)

Type: L3 Worker

Specialized worker auditing logging, monitoring, and observability.

Purpose & Scope

  • Audit observability (Category 10: Medium Priority)
  • Check logging, health checks, metrics, tracing
  • Calculate compliance score (X/10)

Inputs

MANDATORY READ: Load shared/references/audit_worker_core_contract.md. MANDATORY READ: Load shared/references/mcp_tool_preferences.md and shared/references/mcp_integration_patterns.md

Receives contextStore with tech stack, framework, codebase root, output_dir.

Use hex-graph first when traces, call paths, or cross-file references materially improve the audit. Use hex-line first for local code reads when available. If MCP is unavailable, unsupported, or not indexed, continue with built-in Read/Grep/Glob/Bash and state the fallback in the report.

Workflow

MANDATORY READ: Load shared/references/two_layer_detection.md for detection methodology.

  1. Parse context + output_dir
  2. Determine project type (Layer 2 pre-check): Is this a web service (all checks apply), CLI tool (health/probes not applicable), or library (most checks optional)? Adjust applicable checks accordingly.
  3. Check observability patterns (Layer 1: grep)
  4. Analyze context per candidate (Layer 2):

- Structured logging: is this a library (no logging OK) or a service (logging required)? - Health endpoints: web service -> required. CLI/library -> skip - Request tracing: monolith -> less needed. Microservice -> critical

  1. Collect confirmed findings
  2. Calculate score
  3. Write Report: Build full markdown report in memory per shared/templates/audit_worker_report_template.md, write to {output_dir}/ln-627--global.md in single Write call
  4. Return Summary: Return minimal summary to coordinator

Audit Rules

1. Structured Logging

Detection:

  • Grep for console.log (unstructured)
  • Check for proper logger: winston, pino, logrus, zap

Severity:

  • MEDIUM: Production code using console.log
  • LOW: Dev code using console.log

Recommendation: Use structured logger (winston, pino)

Effort: M (add logger, replace calls)

2. Health Check Endpoints

Detection:

  • Grep for /health, /ready, /live routes
  • Check API route definitions

Severity:

  • HIGH: No health check endpoint (monitoring blind spot)

Recommendation: Add /health endpoint

Effort: S (add simple route)

3. Metrics Collection

Detection:

  • Check for Prometheus client, StatsD, CloudWatch
  • Grep for metric recording: histogram, counter

Severity:

  • MEDIUM: No metrics instrumentation

Recommendation: Add Prometheus metrics

Effort: M (instrument code)

4. Request Tracing

Detection:

  • Check for correlation IDs in logs
  • Verify trace propagation (OpenTelemetry, Zipkin)

Severity:

  • MEDIUM: No correlation IDs (hard to debug distributed systems)

Recommendation: Add request ID middleware

Effort: M (add middleware, propagate IDs)

5. Log Levels

Detection:

  • Check if logger supports levels (info, warn, error, debug)
  • Verify proper level usage

Severity:

  • LOW: Only error logging (insufficient visibility)

Recommendation: Add info/debug logs

Effort: S (add log statements)

Scoring Algorithm

MANDATORY READ: Load shared/references/audit_worker_core_contract.md and shared/references/audit_scoring.md.

Output Format

MANDATORY READ: Load shared/references/audit_worker_core_contract.md and shared/templates/audit_worker_report_template.md.

Write JSON summary per shared/references/audit_summary_contract.md. In managed mode the caller passes both runId and summaryArtifactPath; in standalone mode the worker generates its own run-scoped artifact path per shared contract.

Write report to {output_dir}/ln-627--global.md with category: "Observability" and checks: structured_logging, health_endpoints, metrics_collection, request_tracing, log_levels.

Return summary per shared/references/audit_summary_contract.md.

When summaryArtifactPath is absent, write the standalone runtime summary under .hex-skills/runtime-artifacts/runs/{run_id}/evaluation-worker/{worker}--{identifier}.json and optionally echo the same summary in structured output.

Report written: .hex-skills/runtime-artifacts/runs/{run_id}/audit-report/ln-627--global.md
Score: X.X/10 | Issues: N (C:N H:N M:N L:N)

Reference Files

  • Audit output schema: shared/references/audit_output_schema.md

Critical Rules

MANDATORY READ: Load shared/references/audit_worker_core_contract.md.

  • Do not auto-fix: Report only, never inject logging or endpoints
  • Framework-aware detection: Adapt patterns to project's tech stack (winston/pino for Node, logrus/zap for Go, etc.)
  • Effort realism: S = <1h, M = 1-4h, L = >4h
  • Exclusions: Skip test files for console.log detection, skip dev-only scripts
  • Context-sensitive severity: console.log in production code = MEDIUM, in dev utilities = LOW

Definition of Done

MANDATORY READ: Load shared/references/audit_worker_core_contract.md.

  • contextStore parsed (tech stack, framework, output_dir)
  • All 5 checks completed (structured logging, health endpoints, metrics, request tracing, log levels)
  • Findings collected with severity, location, effort, recommendation
  • Score calculated per shared/references/audit_scoring.md
  • Report written to {output_dir}/ln-627--global.md (atomic single Write call)
  • Summary written per contract

Version: 3.0.0 Last Updated: 2025-12-23

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

26.86%
按下载量换算587

Gemini CLI

21.76%
按下载量换算476

Codex

18.06%
按下载量换算395

OpenCode

11.41%
按下载量换算250

Antigravity

7.08%
按下载量换算155

windsurf

3.05%
按下载量换算67

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

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来源信息

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