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自动化审计运维

以证据为先的自动化清单与重叠审计工作流,适用于ECC平台。当用户想要在动手修复前了解哪些作业、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?

这是一个专为ECC平台设计的审计优先型运维技能,其核心任务是在任何重写或修复操作之前,生成一份有证据支持的自动化清单和一套清晰的操作建议(保留/合并/删除/下一步修复)。该技能支持全面的审计工作流,能够对本地运行时脚本、GitHub Actions工作流、MCP配置、连接器集成、包装脚本以及应用自动化入口点进行清单盘点。它通过将每个自动化项分类为“已配置”、“已验证”、“过期/损坏”或“完全缺失”等状态,并以文件路径、工作流运行记录、钩子日志等具体证据为支撑,来保证审计结果的客观性。对于发现的任意重叠或可疑自动化表面,该技能都会给出明确的保留、合并、删除或下一步修复建议。主要应用于CI/CD流水线、工具链集成、应用自动化等场景,帮助用户消除冗余、修复损坏路径并优化工作流。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/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正在运行、哪些作业已损坏、何处存在重叠,或者哪些工具和连接器当前真正在做有用功时,请使用此技能。

这是一个审计优先的操作型技能。其任务是在任何重写操作之前,生成一份有证据支持的清单以及一套保留/合并/删除/下一步修复的建议集。

技能栈(Skill Stack)

在相关时,将这些ECC原生技能纳入工作流:

  • workspace-surface-audit:用于连接器、MCP、钩子和应用清单
  • knowledge-ops:当审计需要将仓库实时真相与持久上下文进行对账时
  • github-ops:当答案依赖于CI、定时工作流、Issue或PR自动化时
  • ecc-tools-cost-audit:当真正问题是Webhook扇出、排队作业或兄弟应用仓库中的计费消耗时
  • research-ops:当本地清单需要与当前平台支持或公共文档进行比较时
  • verification-loop:用于验证修复后的状态,而非依赖假定的恢复

使用时机

  • 用户询问“我有哪些自动化”、“什么在运行”、“什么已损坏”或“什么存在重叠”
  • 任务涉及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是从其他代理系统迁移过来的,以及哪些仍需要在ECC内部重建
  • 工作区积累了多种实现同一功能的方式,用户希望统一为一条规范路径

安全护栏

  • 以只读方式开始,除非用户明确要求修复
  • 区分以下几种状态:
    • 已配置
    • 已验证身份
    • 最近验证通过
    • 过期或损坏
    • 完全缺失
  • 不要仅仅因为某个技能或配置文件引用了某个工具,就声称该工具正在运行
  • 在存在证据表之前,不要合并或删除重叠的自动化表面

工作流

1. 清单盘点真实表面

在进行理论分析之前,先读取当前活跃的自动化表面:

  • 仓库钩子和本地钩子脚本
  • GitHub Actions和定时工作流
  • MCP配置和已启用的服务器
  • 连接器或应用支持的集成
  • 包装脚本和仓库特定的自动化入口点

按表面分组:

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

2. 按运行状态对每个项目分类

对于每个发现的自动化项目,标记其状态:

  • 已配置
  • 已验证身份
  • 最近验证通过
  • 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. 追溯证据路径

为每一个重要声明提供具体来源:

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

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/删除/下一步修复结束

对于每一个重叠或可疑的自动化表面,返回一个判断:

  • 保留
  • 合并
  • 删除
  • 下一步修复

价值在于将杂乱的自动化收敛为一条规范的ECC路径,而非保留每一条历史路径。

输出格式

当前表面
- 自动化
- 来源
- 运行状态
- 证据

发现
- 活跃故障
- 重叠
- 状态过期
- 功能缺失

建议
- 保留
- 合并
- 删除
- 下一步修复

下一步ECC动作
- 要加固的确切技能/钩子/工作流/应用路径

常见陷阱

  • 当可以读取实时清单时,不要仅凭记忆回答
  • 不要将“配置中存在”等同于“正在工作”
  • 在指出高信号路径的故障之前,不要急于修复低价值冗余
  • 如果用户只是要求清单盘点,不要将任务扩大为仓库重写

验证

  • 重要声明引用了一条实时的证据路径
  • 每个发现的自动化项目都标有清晰的运行状态类别
  • 最终建议区分了保留/合并/删除/下一步修复

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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