senior-devops — скилл для Claude Code: установка и задачи
Скилл создаёт CI/CD-пайплайны, Terraform-модули и Kubernetes-манифесты. Подойдёт для развёртывания приложений и проверки инфраструктуры.
/plugin marketplace add alirezarezvani/claude-skills
/plugin install engineering-skills@claude-code-skillsЧто делает senior-devops
senior-devops добавляет три скрипта для DevOps-задач. pipeline_generator.py создаёт CI/CD-конфигурации для GitHub Actions или CircleCI со стадиями сборки, тестирования, проверки безопасности и деплоя. terraform_scaffolder.py генерирует и проверяет модули Terraform для AWS, GCP или Azure.
deployment_manager.py формирует Kubernetes-манифесты и упорядоченные runbook-команды для blue-green или rolling-стратегии. Он добавляет health-check gates перед переключением трафика и сценарии отката, но сам не применяет изменения к кластеру. Для работы нужны Python, Terraform и инструменты окружения, которые используются выбранными командами.
Ставьте скилл, если нужно подготовить пайплайн, инфраструктурный модуль или план деплоя с ручной проверкой. Для задач вне CI/CD, Terraform и Kubernetes он избыточен.
Какие задачи решает
- создать CI/CD-пайплайн для GitHub Actions или CircleCI
- сгенерировать Terraform-модуль для AWS, GCP или Azure
- подготовить Kubernetes-манифесты для blue-green или rolling-деплоя
- сформировать runbook для отката версии
- проверить Terraform-план до применения изменений
Примеры запросов агенту
- Создай CI/CD-пайплайн для `./app` через GitHub Actions со стадиями `build,test,deploy`.
- Сгенерируй модуль Terraform для `./infra` с провайдером AWS и модулем `ecs-service`.
- Подготовь deployment для `staging` с образом `app:1.2.3` и стратегией `blue-green`.
Как установить senior-devops
1Через маркетплейс плагинов
/plugin marketplace add alirezarezvani/claude-skills/plugin install engineering-skills@claude-code-skillsКоманда ставит плагин engineering-skills целиком, со всеми его скиллами
2Через Skills CLI (Vercel)
npx skills add alirezarezvani/claude-skills --skill senior-devops -a claude-code3Вручную: положить файлы
~/.claude/skills/senior-devops/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/senior-devops/
Вручную: положить файлы
.agents/skills/senior-devops/SKILL.mdДля всех проектов — ~/.agents/skills/
Вручную: положить файлы
.cursor/skills/senior-devops/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install alirezarezvani/claude-skills senior-devops2Вручную: положить файлы
.github/skills/senior-devops/SKILL.mdКомандой платформы
gemini skills install https://github.com/alirezarezvani/claude-skills --path engineering-team/skills/senior-devopsВручную: положить файлы
.devin/skills/senior-devops/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Claude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
Из того же набора349
Весь набор →Похожие
Вопросы о senior-devops
Как установить senior-devops в Claude Code?
Выполните npx skills add alirezarezvani/claude-skills --skill senior-devops -a claude-code. Скилл появится по пути ~/.claude/skills/senior-devops/SKILL.md.
Что делает senior-devops?
Он создаёт CI/CD-пайплайны, Terraform-модули, Kubernetes-манифесты и runbook-команды для деплоя и отката.
Применяет ли deployment manager изменения к кластеру?
Нет. Он записывает манифесты и выводит команды, чтобы каждое изменение прошло ручную проверку.
Исходный текст
SKILL.md · MIT
---
name: "senior-devops"
description: Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
---
# Senior Devops
Complete toolkit for senior devops with modern tools and best practices.
## Quick Start
### Main Capabilities
This skill provides three core capabilities through automated scripts:
```bash
# Script 1: Pipeline Generator — scaffolds CI/CD pipelines for GitHub Actions or CircleCI
python scripts/pipeline_generator.py ./app --platform=github --stages=build,test,deploy
# Script 2: Terraform Scaffolder — generates and validates IaC modules for AWS/GCP/Azure
python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose
# Script 3: Deployment Manager — generates deployment manifests + runbooks with rollback support
python3 scripts/deployment_manager.py deploy --env=staging --image=app:1.2.3 --strategy=blue-green --verbose --json
```
## Core Capabilities
### 1. Pipeline Generator
Scaffolds CI/CD pipeline configurations for GitHub Actions or CircleCI, with stages for build, test, security scan, and deploy.
**Example — GitHub Actions workflow:**
```yaml
# .github/workflows/ci.yml
name: CI/CD Pipeline
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
jobs:
build-and-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- run: npm run lint
- run: npm test -- --coverage
- name: Upload coverage
uses: codecov/codecov-action@v4
build-docker:
needs: build-and-test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build and push image
uses: docker/build-push-action@v5
with:
push: ${{ github.ref == 'refs/heads/main' }}
tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
deploy:
needs: build-docker
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
steps:
- name: Deploy to ECS
run: |
aws ecs update-service \
--cluster production \
--service app-service \
--force-new-deployment
```
**Usage:**
```bash
python scripts/pipeline_generator.py <project-path> --platform=github|circleci --stages=build,test,deploy
```
### 2. Terraform Scaffolder
Generates, validates, and plans Terraform modules. Enforces consistent module structure and runs `terraform validate` + `terraform plan` before any apply.
**Example — AWS ECS service module:**
```hcl
# modules/ecs-service/main.tf
resource "aws_ecs_task_definition" "app" {
family = var.service_name
requires_compatibilities = ["FARGATE"]
network_mode = "awsvpc"
cpu = var.cpu
memory = var.memory
container_definitions = jsonencode([{
name = var.service_name
image = var.container_image
essential = true
portMappings = [{
containerPort = var.container_port
protocol = "tcp"
}]
environment = [for k, v in var.env_vars : { name = k, value = v }]
logConfiguration = {
logDriver = "awslogs"
options = {
awslogs-group = "/ecs/${var.service_name}"
awslogs-region = var.aws_region
awslogs-stream-prefix = "ecs"
}
}
}])
}
resource "aws_ecs_service" "app" {
name = var.service_name
cluster = var.cluster_id
task_definition = aws_ecs_task_definition.app.arn
desired_count = var.desired_count
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnet_ids
security_groups = [aws_security_group.app.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.app.arn
container_name = var.service_name
container_port = var.container_port
}
}
```
**Usage:**
```bash
python scripts/terraform_scaffolder.py <target-path> --provider=aws|gcp|azure --module=ecs-service|gke-deployment|aks-service [--verbose]
```
### 3. Deployment Manager
Generates Kubernetes deployment manifests and ordered kubectl runbooks for blue/green or rolling strategies, with health-check gates before traffic switches and rollback runbooks. The tool writes manifests and prints the commands — it never applies them to a cluster itself, so every change gets a human review.
**Example — Kubernetes blue/green deployment (blue-slot specific elements):**
```yaml
# k8s/deployment-blue.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-blue
labels:
app: myapp
slot: blue # slot label distinguishes blue from green
spec:
replicas: 3
selector:
matchLabels:
app: myapp
slot: blue
template:
metadata:
labels:
app: myapp
slot: blue
spec:
containers:
- name: app
image: ghcr.io/org/app:1.2.3
readinessProbe: # gate: pod must pass before traffic switches
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 10
periodSeconds: 5
resources:
requests:
cpu: "250m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
```
**Usage:**
```bash
python scripts/deployment_manager.py deploy \
--env=staging|production \
--image=app:1.2.3 \
--strategy=blue-green|rolling \
--health-check-url=https://app.example.com/healthz
python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2
python scripts/deployment_manager.py --analyze --env=production # audit current state
```
## Resources
- Pattern Reference: `references/cicd_pipeline_guide.md` — detailed CI/CD patterns, best practices, anti-patterns
- Workflow Guide: `references/infrastructure_as_code.md` — IaC step-by-step processes, optimization, troubleshooting
- Technical Guide: `references/deployment_strategies.md` — deployment strategy configs, security considerations, scalability
- Tool Scripts: `scripts/` directory
## Development Workflow
### 1. Infrastructure Changes (Terraform)
```bash
# Scaffold or update module
python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose
# Validate and plan — review diff before applying
terraform -chdir=infra init
terraform -chdir=infra validate
terraform -chdir=infra plan -out=tfplan
# Apply only after plan review
terraform -chdir=infra apply tfplan
# Verify resources are healthy
aws ecs describe-services --cluster production --services app-service \
--query 'services[0].{Status:status,Running:runningCount,Desired:desiredCount}'
```
### 2. Application Deployment
```bash
# Generate or update pipeline config
python scripts/pipeline_generator.py . --platform=github --stages=build,test,security,deploy
# Build and tag image
docker build -t ghcr.io/org/app:$(git rev-parse --short HEAD) .
docker push ghcr.io/org/app:$(git rev-parse --short HEAD)
# Deploy with health-check gate
python scripts/deployment_manager.py deploy \
--env=production \
--image=app:$(git rev-parse --short HEAD) \
--strategy=blue-green \
--health-check-url=https://app.example.com/healthz
# Verify pods are running
kubectl get pods -n production -l app=myapp
kubectl rollout status deployment/app-blue -n production
# Switch traffic after verification
kubectl patch service app-svc -n production \
-p '{"spec":{"selector":{"slot":"blue"}}}'
```
### 3. Rollback Procedure
```bash
# Immediate rollback via deployment manager
python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2
# Or via kubectl
kubectl rollout undo deployment/app -n production
kubectl rollout status deployment/app -n production
# Verify rollback succeeded
kubectl get pods -n production -l app=myapp
curl -sf https://app.example.com/healthz || echo "ROLLBACK FAILED — escalate"
```
## Multi-Cloud Cross-References
Use these companion skills for cloud-specific deep dives:
| Skill | Cloud | Use When |
|-------|-------|----------|
| **aws-solution-architect** | AWS | ECS/EKS, Lambda, VPC design, cost optimization |
| **azure-cloud-architect** | Azure | AKS, App Service, Virtual Networks, Azure DevOps |
| **gcp-cloud-architect** | GCP | GKE, Cloud Run, VPC, Cloud Build *(coming soon)* |
**Multi-cloud vs single-cloud decision:**
- **Single-cloud** (default) — lower operational complexity, deeper managed-service integration, better cost leverage with committed-use discounts
- **Multi-cloud** — required when mandated by compliance/data residency, acquiring companies on different clouds, or needing best-of-breed services across providers (e.g., AWS for compute + GCP for ML)
- **Hybrid** — on-prem + cloud; use when regulated workloads must stay on-prem while burst/non-sensitive workloads run in the cloud
> Start single-cloud. Add a second cloud only when there is a concrete business or compliance driver — not for theoretical redundancy.
---
## Cloud-Agnostic IaC
### Terraform / OpenTofu (Default Choice)
Terraform (or its open-source fork OpenTofu) is the recommended IaC tool for most teams:
- Single language (HCL) across AWS, Azure, GCP, and 3,000+ providers
- State management with remote backends (S3, GCS, Azure Blob)
- Plan-before-apply workflow prevents drift surprises
- Cross-reference **terraform-patterns** for module structure, state isolation, and CI/CD integration
### Pulumi (Programming Language IaC)
Choose Pulumi when the team strongly prefers TypeScript, Python, Go, or C# over HCL:
- Full programming language — loops, conditionals, unit tests native
- Same cloud provider coverage as Terraform
- Easier onboarding for dev teams that resist learning HCL
### When to Use Cloud-Native IaC
| Tool | Use When |
|------|----------|
| **CloudFormation** | AWS-only shop; need native AWS support (StackSets, Service Catalog) |
| **Bicep** | Azure-only shop; simpler syntax than ARM templates |
| **Cloud Deployment Manager** | GCP-only; rare — most GCP teams prefer Terraform |
> **Rule of thumb:** Use Terraform/OpenTofu unless you are 100% committed to a single cloud AND the cloud-native tool offers a feature Terraform cannot replicate (e.g., AWS Service Catalog integration).
---
## Troubleshooting
Check the comprehensive troubleshooting section in `references/deployment_strategies.md`.
© Alireza Rezvani · MIT · alirezarezvani/claude-skills