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Infrastructure Devops Devops Engineer

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chendongqi
infrastructure-devops-devops-engineer

DevOps助手 - 专业的DevOps实践与自动化专家。适用场景: (1) CI/CD流水线设计与实现 (2) 部署策略与发布管理 (3) 基础设施即代码(IaC) (4) 容器化与Kubernetes部署 (5) 监控告警与可观测性 (6) DevOps工具链选型 (7) DevOps文化与实践推广 触发关键词:DevOps、CI/CD、持续集成、持续部署、Jenkins、GitLab CI、GitHub Actions、Docker、Kubernetes、Terraform、自动化部署

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill nameinfrastructure-devops-devops-engineer
Stars
133
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chendongqi on GitHub. Read the source before you install it.

Installation

Install the Infrastructure Devops Devops Engineer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/infrastructure-devops-devops-engineer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/infrastructure-devops-devops-engineer .claude/skills/infrastructure-devops-devops-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Infrastructure Devops Devops Engineer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Infrastructure Devops Devops Engineer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Infrastructure Devops Devops Engineer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DevOps助手

核心工作流程

1. CI/CD流水线设计

CI/CD流水线阶段:

持续集成(CI)
├── 代码提交
│   ├── 代码规范检查(Lint)
│   ├── 提交信息规范
│   └── 触发构建
├── 自动构建
│   ├── 依赖安装
│   ├── 编译构建
│   └── 单元测试
├── 质量门禁
│   ├── 代码覆盖率
│   ├── 静态代码分析
│   └── 安全扫描
└── 制品管理
    ├── 版本标记
    ├── 制品上传
    └── 元数据记录

持续部署(CD)
├── 环境准备
│   ├── 基础设施配置
│   ├── 配置管理
│   └── 密钥注入
├── 部署执行
│   ├── 部署策略执行
│   ├── 健康检查
│   └── 冒烟测试
├── 验证确认
│   ├── 自动化测试
│   ├── 性能验证
│   └── 人工确认(可选)
└── 发布完成
    ├── 流量切换
    ├── 监控确认
    └── 回滚准备

流水线配置示例(GitLab CI):

yaml
stages:
  - build
  - test
  - security
  - deploy

build:
  stage: build
  script:
    - npm install
    - npm run build
  artifacts:
    paths:
      - dist/

test:
  stage: test
  script:
    - npm run test:coverage
  coverage: '/Coverage: \d+%/'

security_scan:
  stage: security
  script:
    - npm audit
    - trivy image $IMAGE

deploy_staging:
  stage: deploy
  environment: staging
  script:
    - kubectl apply -f k8s/
  only:
    - develop

deploy_prod:
  stage: deploy
  environment: production
  script:
    - kubectl apply -f k8s/
  when: manual
  only:
    - main

2. 部署策略

部署策略对比:

策略特点风险适用场景
滚动部署逐步替换实例中无状态服务
蓝绿部署两套环境切换低需要快速回滚
金丝雀部署小流量验证低需要灰度验证
A/B测试按特征分流低功能对比测试
重建部署先停后启高开发/测试环境

部署策略详解:

滚动部署(Rolling)
├── 逐批替换旧版本Pod
├── 配置:maxSurge/maxUnavailable
├── 优点:资源占用少
└── 缺点:回滚较慢

蓝绿部署(Blue-Green)
├── 同时运行两套环境
├── 切换负载均衡指向
├── 优点:快速回滚
└── 缺点:资源成本翻倍

金丝雀部署(Canary)
├── 小比例流量到新版本
├── 逐步增加流量比例
├── 优点:风险最小
└── 缺点:实现复杂

渐进式交付(Progressive)
├── 自动化金丝雀+分析
├── 基于指标自动推进/回滚
├── 工具:Argo Rollouts/Flagger
└── 优点:智能化、低风险

3. 基础设施即代码(IaC)

IaC工具选型:

配置管理
├── Ansible:无代理、YAML
├── Chef:Ruby DSL、中心化
├── Puppet:声明式、大规模
└── SaltStack:高性能、Python

基础设施编排
├── Terraform:多云、状态管理
├── Pulumi:通用编程语言
├── CloudFormation:AWS原生
└── ARM/Bicep:Azure原生

容器编排
├── Kubernetes:容器编排标准
├── Docker Compose:单机编排
├── Helm:K8s包管理
└── Kustomize:K8s配置管理

Terraform最佳实践:

目录结构
├── modules/           # 可复用模块
│   ├── vpc/
│   ├── ecs/
│   └── rds/
├── environments/      # 环境配置
│   ├── dev/
│   ├── staging/
│   └── prod/
├── main.tf           # 主配置
├── variables.tf      # 变量定义
├── outputs.tf        # 输出定义
└── backend.tf        # 状态后端

代码规范
├── 使用模块化设计
├── 变量使用类型约束
├── 敏感信息使用变量/Vault
├── 资源命名规范统一
└── 状态文件远程存储

4. 容器化与Kubernetes

容器化最佳实践:

Dockerfile优化
├── 使用多阶段构建
├── 最小化基础镜像
├── 合并RUN指令
├── 使用.dockerignore
├── 非root用户运行
└── 固定依赖版本

镜像安全
├── 扫描已知漏洞
├── 签名验证
├── 使用可信基础镜像
└── 定期更新基础镜像

Kubernetes部署配置:

yaml
# Deployment示例
apiVersion: apps/v1
kind: Deployment
metadata:
  name: app
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    spec:
      containers:
      - name: app
        image: app:v1.0
        resources:
          requests:
            cpu: 100m
            memory: 128Mi
          limits:
            cpu: 500m
            memory: 512Mi
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5

5. 监控与可观测性

可观测性三支柱:

Metrics(指标)
├── 系统指标:CPU/内存/磁盘/网络
├── 应用指标:QPS/延迟/错误率
├── 业务指标:订单数/转化率
├── 工具:Prometheus + Grafana
└── 最佳实践:RED/USE方法

Logs(日志)
├── 结构化日志
├── 日志级别规范
├── 关联追踪ID
├── 工具:ELK/Loki
└── 最佳实践:统一格式、集中管理

Traces(链路追踪)
├── 分布式追踪
├── 服务调用链
├── 性能瓶颈定位
├── 工具:Jaeger/Zipkin/SkyWalking
└── 最佳实践:采样策略、上下文传递

告警设计原则:

原则说明实践
可行动收到告警能采取行动避免无意义告警
分级不同严重程度不同处理P1/P2/P3分级
聚合相似告警合并避免告警风暴
抑制依赖故障不重复告警告警依赖关系
静默计划维护期间静默维护窗口

6. DevOps工具链

工具链选型:

领域开源方案商业方案
版本控制GitLab/GiteaGitHub/Bitbucket
CI/CDJenkins/GitLab CICircleCI/Travis CI
制品管理Nexus/HarborJFrog Artifactory
配置管理Ansible/TerraformPuppet Enterprise
监控Prometheus/GrafanaDatadog/New Relic
日志ELK/LokiSplunk/Sumo Logic
APMSkyWalking/JaegerDynatrace/AppDynamics

DevOps成熟度模型:

Level 1: 初始
├── 手工部署
├── 无版本控制
└── 无自动化测试

Level 2: 可重复
├── 版本控制
├── 基本CI
└── 脚本化部署

Level 3: 已定义
├── 自动化CI/CD
├── 基础设施即代码
└── 自动化测试

Level 4: 可管理
├── 监控告警完善
├── 发布流程规范
└── 度量指标

Level 5: 优化
├── 持续改进
├── 自动化决策
└── 混沌工程

输出模板

CI/CD流水线设计文档

【项目名称】___________
【设计日期】___________

【流水线概览】
[流水线图]

【阶段详情】
| 阶段 | 任务 | 工具 | 超时 | 失败处理 |
|------|------|------|------|----------|

【环境配置】
| 环境 | 触发条件 | 审批 | 部署策略 |
|------|----------|------|----------|

【质量门禁】
| 检查项 | 阈值 | 阻断/警告 |
|--------|------|----------|

【制品管理】
- 制品类型:
- 存储位置:
- 保留策略:

【回滚策略】

部署方案文档

【应用名称】___________
【部署环境】___________

【部署架构】
[部署架构图]

【部署策略】
- 策略类型:□滚动 □蓝绿 □金丝雀
- 具体配置:

【资源配置】
| 资源 | 规格 | 副本数 | 说明 |
|------|------|--------|------|

【健康检查】
- 就绪探针:
- 存活探针:
- 启动探针:

【发布流程】
1.
2.
3.

【回滚流程】
1.
2.

【监控告警】
| 指标 | 阈值 | 告警级别 |
|------|------|----------|

最佳实践

  • 自动化一切:能自动化的都自动化
  • 小步快跑:频繁小发布优于大爆炸
  • 失败快速:尽早发现问题,快速修复
  • 持续反馈:监控指标驱动改进
  • 文化先行:技术工具之前是文化转变

Frequently asked questions

What does the Infrastructure Devops Devops Engineer AI skill do?

DevOps助手 - 专业的DevOps实践与自动化专家。适用场景: (1) CI/CD流水线设计与实现 (2) 部署策略与发布管理 (3) 基础设施即代码(IaC) (4) 容器化与Kubernetes部署 (5) 监控告警与可观测性 (6) DevOps工具链选型 (7) DevOps文化与实践推广 触发关键词:DevOps、CI/CD、持续集成、持续部署、Jenkins、GitLab CI、GitHub Actions、Docker、Kubernetes、Terraform、自动化部署

Why use Infrastructure Devops Devops Engineer on TypingMind?

Because you install it once and use it with any model. Infrastructure Devops Devops Engineer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Infrastructure Devops Devops Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/infrastructure-devops-devops-engineer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Infrastructure Devops Devops Engineer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Infrastructure Devops Devops Engineer?

As many as you like. As long as a model supports skills, you can use Infrastructure Devops Devops Engineer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Infrastructure Devops Devops Engineer AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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