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Devops guide

Skill genapohub/devops-guide

DevOps/运维工程师方案产出指南。当用户提出运维、CI/CD、容器化、云基础设施、监控告警相关需求时触发。覆盖场景：基础设施搭建、CI/CD流程设计与优化、容器化编排、云架构设计、监控日志体系搭建、灾备方案、IaC、成本优化。先识别需求属于5类场景中的哪一类（0到1基础设施搭建/中型运维改造/小优化故障修复/大版本架构迁移/技术预研选型），再按对应场景的产出清单生成完整方案。触发词：CI/CD、DevOps、Kubernetes、Docker、监控、告警、日志、云架构、容器化、Terraform、灾备、高可用、成本优化、运维方案。From its SKILL.md

Install
npx -y skills add genapohub/devops-guide

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  • 18 days oldThe repository was created 18 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.3 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it

DevOps / 运维工程师方案产出指南

Overview

本技能将 DevOps 领域的方法论转化为可执行的工作流。当用户提出运维相关需求时，先识别该需求属于 5 类场景中的哪一类，再按对应场景的产出清单生成完整方案——从基础设施搭建到灾备方案，覆盖运维工程的全生命周期。

详细的方法论、各场景产出清单、CI/CD规范、K8s最佳实践、监控SLO、安全基线、质量检查清单均存放在 references/DevOps方法论.md，在执行前必须读取对应章节。

触发条件

  • 用户需要搭建 CI/CD Pipeline 或改造现有流程
  • 用户需要设计容器化方案或 Kubernetes 配置
  • 用户需要搭建监控、日志、告警体系
  • 用户需要云基础设施规划或迁移
  • 用户提到"CI/CD""DevOps""K8s""Docker""监控告警""日志""灾备""IaC""Terraform""运维"等关键词

执行流程

按以下 5 步顺序执行，不可跳步。

Step 1: 需求理解

  • 解析用户输入的运维需求
  • 提取关键信息：基础设施现状、云平台、容器化程度、监控现状、团队规模、可用性目标、预算约束
  • 识别缺失的关键信息，主动提问补全（一次最多 2-3 个问题）

Step 2: 场景识别

读取 references/DevOps方法论.md 的"一、场景识别"章节判断场景：

场景名称判断条件产出量
场景一0→1 基础设施搭建全新项目、无CI/CD、无监控10-12类
场景二中型运维改造/优化已有基础设施新增模块、CI/CD改造6-8类
场景三小优化/故障修复单条Pipeline修复、告警调整2-3类
场景四大版本架构迁移云平台迁移、K8s升级、工具链替换8-10类
场景五技术预研/选型新工具评估、成本优化PoC3-4类

Step 3: 与用户确认场景

输出场景判断、判断依据、产出清单、预估周期，确认后进入产出。

Step 4: 按清单产出方案

读取 references/DevOps方法论.md 对应场景章节。

产出要求：

  • 架构图使用 Mermaid 或 ASCII 描述（网络拓扑/部署拓扑/数据流）
  • Pipeline 定义给出完整的 YAML 配置示例（GitHub Actions/GitLab CI）
  • K8s 配置给出完整的 YAML manifest（Deployment/Service/HPA/PDB）
  • 监控告警给出具体指标 + 阈值 + 通知方式
  • 遵循"七、DevOps 通用规范"
  • 产出后保存为 Markdown 文件

Step 5: 质量检查

读取 references/DevOps方法论.md 的"八、产出质量检查清单"：

  • CI/CD Pipeline 覆盖 Build→Test→Scan→Deploy 全流程
  • 容器化符合最佳实践（多阶段构建/非root/资源限制）
  • 监控覆盖 Metrics + Logs + Traces
  • 告警分级明确 + 通知渠道
  • 灾备方案含 RPO/RTO + 演练计划
  • 安全覆盖镜像/网络/密钥/审计
  • 回滚方案可执行

资源说明

references/DevOps方法论.md

完整的方法论文档，包含：5个场景产出清单、CI/CD规范、Docker最佳实践、K8s资源规范、监控SLO参考、安全基线、质量检查清单。

注意事项

  • 不要跳过 Step 3 的用户确认
  • Pipeline 必须覆盖安全性检查（镜像扫描/依赖扫描）
  • 容器必须配置资源限制（Request/Limit），不加限制是安全隐患
  • 告警必须分级 + 指定通知渠道 + 定义升级策略
  • 灾备方案不能停在纸面，必须含演练计划
  • 场景四（架构迁移）的回滚预案是硬性要求

What ships with it: 5 files

20.1 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most containers cloud skills give in ~1.4k tokens

Counted across 607 of the 657 authors here whose files we hold, read 2026-08-07

  • Run containers as a non-root userin 66 of 607, across 46 files
  • Use multi-stage buildsin 53 of 607, across 44 files
  • Use Promise.all for independent operationsin 47 of 607, across 13 files
  • Import directly instead of barrel filesin 46 of 607, across 12 files
  • Use ternary instead of AND for conditionalsin 45 of 607, across 12 files
  • Use Set or Map for O(1) lookupsin 42 of 607, across 10 files
  • Create a .dockerignore filein 41 of 607, across 31 files
  • Read individual rule files for detailsin 39 of 607, across 9 files
  • Copy dependency files before source codein 36 of 607, across 23 files
  • Authenticate server actions like API routesin 35 of 607, across 7 files
  • Use next/dynamic for heavy componentsin 34 of 607, across 9 files
  • Use React.cache for per-request deduplicationin 34 of 607, across 10 files

Said here and by no other author read

  • identify the operational scenario from five types
  • extract current infrastructure and constraints from input
  • ask 2 to 3 questions to fill missing information
  • confirm the scenario with the user before proceeding
  • generate the plan per the scenario checklist
  • use mermaid or ascii for architecture diagrams

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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