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自动化审计运维

基于证据的自动化清单与重叠审计工作流,适用于ECC环境。当用户想知道哪些作业、钩子、连接器、MCP服务器或包装器是活跃的、损坏的、冗余的或缺失时使用。

自动化审计运维 是什么?

自动化审计运维(Automation Audit Ops)是一个以审计为先的操作型技能,专为ECC平台设计。它的核心目标不是立即修复问题,而是生成一份基于证据的自动化清单,并针对每个发现的项提出保留、合并、移除或修复建议。该技能支持多种平台和工具,包括GitHub Actions(CI/调度工作流)、MCP服务器配置、连接器与应用程序集成、本地仓库钩子脚本以及包装器脚本等。工作流程分为四个步骤:首先,全面盘点当前活跃的自动化表面;其次,对每个项进行状态分类(已配置、已验证、已过时等);然后,为每个重要声明追踪具体的证据路径(如文件路径、工作流运行日志等);最后,为每个重叠或可疑的表面输出明确的建议。该技能特别强调证据优先,禁止仅凭配置引用就断定工具活跃,并且要求将成本审计、知识审计等辅助技能按需纳入工作流中。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/automation-audit-ops

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

自动化审计运维 是做什么的?

当用户询问哪些自动化是活跃的、哪些作业损坏、哪里存在重叠,或者哪些工具和连接器当前真正在做有用工作时,使用此技能。

这是一个以审计为先的操作技能。其任务是在重写任何内容之前,生成一份基于证据的清单以及一份保留/合并/移除/后续修复的推荐集。

技能栈

在相关时将以下ECC原生技能加入工作流:

  • workspace-surface-audit:用于连接器、MCP、钩子和应用程序清单
  • knowledge-ops:当审计需要将实时仓库真相与持久上下文对齐时
  • github-ops:当答案取决于CI、计划工作流、议题或PR自动化时
  • ecc-tools-cost-audit:当实际问题在于兄弟应用仓库中的webhook扇出、队列作业或计费消耗时
  • research-ops:当本地清单需要与当前平台支持或公开文档进行比较时
  • verification-loop:用于验证修复后的状态,而不是依赖假设的恢复

使用场景

  • 用户询问“我有哪些自动化”、“什么是活跃的”、“什么损坏了”或“哪里重叠”
  • 任务涵盖cron作业、GitHub Actions、本地钩子、MCP服务器、连接器、包装器或应用集成
  • 用户想知道哪些内容从另一个代理系统移植过来,哪些仍需要在ECC内部重建
  • 工作空间中积累了多种实现相同功能的方式,用户希望保留单一规范路径

限制原则

  • 除非用户明确要求修复,否则以只读模式开始
  • 区分:
    • 已配置
    • 已验证身份
    • 最近已验证
    • 已过期或损坏
    • 完全缺失
  • 不要仅仅因为技能或配置文件引用了某个工具,就声称该工具活跃
  • 在证据表建立之前,不要合并或删除重叠的表面

工作流

1. 盘点真实表面

在理论化之前,先读取当前活跃的表面:

  • 仓库钩子和本地钩子脚本
  • GitHub Actions和计划工作流
  • MCP配置和已启用的服务器
  • 连接器或应用支持集成的项
  • 包装器脚本和特定于仓库的自动化入口点

按表面进行分组:

  • 本地运行时
  • 仓库CI/自动化
  • 连接的外部系统
  • 消息/通知
  • 计费/客户操作
  • 研究/监控

2. 对每个项按活跃状态分类

对每个浮现的自动化项,标记:

  • 已配置
  • 已验证身份
  • 最近已验证
  • 已过期或损坏
  • 缺失

然后分类问题类型:

  • 活跃故障
  • 身份验证中断
  • 状态过期
  • 重叠或冗余
  • 功能缺失

3. 追踪证据路径

用具体来源支持每个重要声明:

  • 文件路径
  • 工作流运行
  • 钩子日志
  • 配置条目
  • 最近的命令输出
  • 确切的失败签名

如果当前状态不明确,直接说明,而不是假装审计已完成。

4. 以保留/合并/移除/后续修复结束

对每个重叠或可疑的表面,返回一个动作:

  • 保留
  • 合并
  • 移除
  • 后续修复

价值在于将杂乱的自动化折叠到一条规范的ECC通道中,而不是保留每一条历史路径。

输出格式

当前表面
- 自动化项
- 来源
- 活跃状态
- 证据

发现项
- 活跃故障
- 重叠
- 状态过期
- 功能缺失

建议
- 保留
- 合并
- 移除
- 后续修复

下一步ECC行动
- 要加固的确切技能/钩子/工作流/应用通道

陷阱

  • 当可以读取实时清单时,不要仅凭记忆作答。
  • 不要将“配置中存在”视为“正常工作”。
  • 在命名损坏的高信号路径之前,不要先修复低价值的冗余。
  • 如果用户只要求清单,不要将任务扩大为仓库重写。

验证

  • 重要声明引用实时证据路径。
  • 每个浮现的自动化项都标有清晰的活跃状态类别。
  • 最终建议区分保留/合并/移除/后续修复。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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