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devops-deploy

DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento.

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Install

skills CLI npx skills add https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/devops-deploy
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install sickn33-agentic-awesome-skills@llmmart
Git git clone https://github.com/sickn33/agentic-awesome-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole sickn33/agentic-awesome-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

DEVOPS-DEPLOY — Da Ideia para Producao

Overview

DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento. Ativar para: dockerizar aplicacao, configurar pipeline CI/CD, deploy na AWS, Lambda, ECS, configurar GitHub Actions, Terraform, rollback, blue-green deploy, health checks, alertas.

When to Use This Skill

  • When you need specialized assistance with this domain

Do Not Use This Skill When

  • The task is unrelated to devops deploy
  • A simpler, more specific tool can handle the request
  • The user needs general-purpose assistance without domain expertise

How It Works

"Move fast and don't break things." — Engenharia de elite nao e lenta. E rapida e confiavel ao mesmo tempo.


Dockerfile Otimizado (Python)

FROM python:3.11-slim AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt

FROM python:3.11-slim
WORKDIR /app
COPY --from=builder /root/.local /root/.local
COPY . .
ENV PATH=/root/.local/bin:$PATH
ENV PYTHONUNBUFFERED=1
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:8000/health || exit 1
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Docker Compose (Dev Local)

version: "3.9"
services:
  app:
    build: .
    ports: ["8000:8000"]
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    volumes:
      - .:/app
    depends_on: [db, redis]
  db:
    image: postgres:15
    environment:
      POSTGRES_DB: auri
      POSTGRES_USER: auri
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - pgdata:/var/lib/postgresql/data
  redis:
    image: redis:7-alpine
volumes:
  pgdata:

Sam Template (Serverless)


## Template.Yaml

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Globals:
  Function:
    Timeout: 30
    Runtime: python3.11
    Environment:
      Variables:
        ANTHROPIC_API_KEY: !Ref AnthropicApiKey
        DYNAMODB_TABLE: !Ref AuriTable

Resources:
  AuriFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: src/
      Handler: lambda_function.handler
      MemorySize: 512
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref AuriTable

  AuriTable:
    Type: AWS::DynamoDB::Table
    Properties:
      TableName: auri-users
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: userId
          AttributeType: S
      KeySchema:
        - AttributeName: userId
          KeyType: HASH
      TimeToLiveSpecification:
        AttributeName: ttl
        Enabled: true

Deploy Commands


## Build E Deploy

sam build
sam deploy --guided  # primeira vez
sam deploy           # deploys seguintes

## Deploy Rapido (Sem Confirmacao)

sam deploy --no-confirm-changeset --no-fail-on-empty-changeset

## Ver Logs Em Tempo Real

sam logs -n AuriFunction --tail

## Deletar Stack

sam delete

.Github/Workflows/Deploy.Yml

name: Deploy Auri

on: push: branches: [main] pull_request: branches: [main]

jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: { python-version: "3.11" } - run: pip install -r requirements.txt - run: pytest tests/ -v --cov=src --cov-report=xml - uses: codecov/codecov-action@v4

security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - run: pip install bandit safety - run: bandit -r src/ -ll - run: safety check -r requirements.txt

deploy: needs: [test, security] if: github.ref == 'refs/heads/main' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: aws-actions/setup-sam@v2 - uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: us-east-1 - run: sam build - run: sam deploy --no-confirm-changeset - name: Notify Telegram on Success run: | curl -s -X POST "https://api.telegram.org/bot${{ secrets.TELEGRAM_BOT_TOKEN }}/sendMessage"
-d "chat_id=${{ secrets.TELEGRAM_CHAT_ID }}"
-d "text=Auri deployed successfully! Commit: ${{ github.sha }}"


---

## Health Check Endpoint

```python
from fastapi import FastAPI
import time, os

app = FastAPI()
START_TIME = time.time()

@app.get("/health")
async def health():
    return {
        "status": "healthy",
        "uptime_seconds": time.time() - START_TIME,
        "version": os.environ.get("APP_VERSION", "unknown"),
        "environment": os.environ.get("ENV", "production")
    }

Alertas Cloudwatch

import boto3

def create_error_alarm(function_name: str, sns_topic_arn: str):
    cw = boto3.client("cloudwatch")
    cw.put_metric_alarm(
        AlarmName=f"{function_name}-errors",
        MetricName="Errors",
        Namespace="AWS/Lambda",
        Dimensions=[{"Name": "FunctionName", "Value": function_name}],
        Period=300,
        EvaluationPeriods=1,
        Threshold=5,
        ComparisonOperator="GreaterThanThreshold",
        AlarmActions=[sns_topic_arn],
        TreatMissingData="notBreaching"
    )

5. Checklist De Producao

  • Variaveis de ambiente via Secrets Manager (nunca hardcoded)
  • Health check endpoint respondendo
  • Logs estruturados (JSON) com request_id
  • Rate limiting configurado
  • CORS restrito a dominios autorizados
  • DynamoDB com backup automatico ativado
  • Lambda com timeout adequado (10-30s)
  • CloudWatch alarmes para erros e latencia
  • Rollback plan documentado
  • Load test antes do lancamento

6. Comandos

Comando Acao
/docker-setup Dockeriza a aplicacao
/sam-deploy Deploy completo na AWS Lambda
/ci-cd-setup Configura GitHub Actions pipeline
/monitoring-setup Configura CloudWatch e alertas
/production-checklist Roda checklist pre-lancamento
/rollback Plano de rollback para versao anterior

Best Practices

  • Provide clear, specific context about your project and requirements
  • Review all suggestions before applying them to production code
  • Combine with other complementary skills for comprehensive analysis

Common Pitfalls

  • Using this skill for tasks outside its domain expertise
  • Applying recommendations without understanding your specific context
  • Not providing enough project context for accurate analysis

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Files (agentic-awesome-skills)
  • SKILL.md 7.2 KB
    ---
    name: devops-deploy
    description: "DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento."
    risk: critical
    source: community
    date_added: '2026-03-06'
    author: renat
    tags:
    - devops
    - docker
    - ci-cd
    - aws
    - terraform
    - github-actions
    tools:
    - claude-code
    - antigravity
    - cursor
    - gemini-cli
    - codex-cli
    ---
    
    # DEVOPS-DEPLOY — Da Ideia para Producao
    
    ## Overview
    
    DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento. Ativar para: dockerizar aplicacao, configurar pipeline CI/CD, deploy na AWS, Lambda, ECS, configurar GitHub Actions, Terraform, rollback, blue-green deploy, health checks, alertas.
    
    ## When to Use This Skill
    
    - When you need specialized assistance with this domain
    
    ## Do Not Use This Skill When
    
    - The task is unrelated to devops deploy
    - A simpler, more specific tool can handle the request
    - The user needs general-purpose assistance without domain expertise
    
    ## How It Works
    
    > "Move fast and don't break things." — Engenharia de elite nao e lenta.
    > E rapida e confiavel ao mesmo tempo.
    
    ---
    
    ## Dockerfile Otimizado (Python)
    
    ```dockerfile
    FROM python:3.11-slim AS builder
    WORKDIR /app
    COPY requirements.txt .
    RUN pip install --no-cache-dir --user -r requirements.txt
    
    FROM python:3.11-slim
    WORKDIR /app
    COPY --from=builder /root/.local /root/.local
    COPY . .
    ENV PATH=/root/.local/bin:$PATH
    ENV PYTHONUNBUFFERED=1
    EXPOSE 8000
    HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:8000/health || exit 1
    CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
    ```
    
    ## Docker Compose (Dev Local)
    
    ```yaml
    version: "3.9"
    services:
      app:
        build: .
        ports: ["8000:8000"]
        environment:
          - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
        volumes:
          - .:/app
        depends_on: [db, redis]
      db:
        image: postgres:15
        environment:
          POSTGRES_DB: auri
          POSTGRES_USER: auri
          POSTGRES_PASSWORD: ${DB_PASSWORD}
        volumes:
          - pgdata:/var/lib/postgresql/data
      redis:
        image: redis:7-alpine
    volumes:
      pgdata:
    ```
    
    ---
    
    ## Sam Template (Serverless)
    
    ```yaml
    
    ## Template.Yaml
    
    AWSTemplateFormatVersion: '2010-09-09'
    Transform: AWS::Serverless-2016-10-31
    
    Globals:
      Function:
        Timeout: 30
        Runtime: python3.11
        Environment:
          Variables:
            ANTHROPIC_API_KEY: !Ref AnthropicApiKey
            DYNAMODB_TABLE: !Ref AuriTable
    
    Resources:
      AuriFunction:
        Type: AWS::Serverless::Function
        Properties:
          CodeUri: src/
          Handler: lambda_function.handler
          MemorySize: 512
          Policies:
            - DynamoDBCrudPolicy:
                TableName: !Ref AuriTable
    
      AuriTable:
        Type: AWS::DynamoDB::Table
        Properties:
          TableName: auri-users
          BillingMode: PAY_PER_REQUEST
          AttributeDefinitions:
            - AttributeName: userId
              AttributeType: S
          KeySchema:
            - AttributeName: userId
              KeyType: HASH
          TimeToLiveSpecification:
            AttributeName: ttl
            Enabled: true
    ```
    
    ## Deploy Commands
    
    ```bash
    
    ## Build E Deploy
    
    sam build
    sam deploy --guided  # primeira vez
    sam deploy           # deploys seguintes
    
    ## Deploy Rapido (Sem Confirmacao)
    
    sam deploy --no-confirm-changeset --no-fail-on-empty-changeset
    
    ## Ver Logs Em Tempo Real
    
    sam logs -n AuriFunction --tail
    
    ## Deletar Stack
    
    sam delete
    ```
    
    ---
    
    ## .Github/Workflows/Deploy.Yml
    
    name: Deploy Auri
    
    on:
      push:
        branches: [main]
      pull_request:
        branches: [main]
    
    jobs:
      test:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v4
          - uses: actions/setup-python@v5
            with: { python-version: "3.11" }
          - run: pip install -r requirements.txt
          - run: pytest tests/ -v --cov=src --cov-report=xml
          - uses: codecov/codecov-action@v4
    
      security:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v4
          - run: pip install bandit safety
          - run: bandit -r src/ -ll
          - run: safety check -r requirements.txt
    
      deploy:
        needs: [test, security]
        if: github.ref == 'refs/heads/main'
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v4
          - uses: aws-actions/setup-sam@v2
          - uses: aws-actions/configure-aws-credentials@v4
            with:
              aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
              aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
              aws-region: us-east-1
          - run: sam build
          - run: sam deploy --no-confirm-changeset
          - name: Notify Telegram on Success
            run: |
              curl -s -X POST "https://api.telegram.org/bot${{ secrets.TELEGRAM_BOT_TOKEN }}/sendMessage" \
                -d "chat_id=${{ secrets.TELEGRAM_CHAT_ID }}" \
                -d "text=Auri deployed successfully! Commit: ${{ github.sha }}"
    ```
    
    ---
    
    ## Health Check Endpoint
    
    ```python
    from fastapi import FastAPI
    import time, os
    
    app = FastAPI()
    START_TIME = time.time()
    
    @app.get("/health")
    async def health():
        return {
            "status": "healthy",
            "uptime_seconds": time.time() - START_TIME,
            "version": os.environ.get("APP_VERSION", "unknown"),
            "environment": os.environ.get("ENV", "production")
        }
    ```
    
    ## Alertas Cloudwatch
    
    ```python
    import boto3
    
    def create_error_alarm(function_name: str, sns_topic_arn: str):
        cw = boto3.client("cloudwatch")
        cw.put_metric_alarm(
            AlarmName=f"{function_name}-errors",
            MetricName="Errors",
            Namespace="AWS/Lambda",
            Dimensions=[{"Name": "FunctionName", "Value": function_name}],
            Period=300,
            EvaluationPeriods=1,
            Threshold=5,
            ComparisonOperator="GreaterThanThreshold",
            AlarmActions=[sns_topic_arn],
            TreatMissingData="notBreaching"
        )
    ```
    
    ---
    
    ## 5. Checklist De Producao
    
    - [ ] Variaveis de ambiente via Secrets Manager (nunca hardcoded)
    - [ ] Health check endpoint respondendo
    - [ ] Logs estruturados (JSON) com request_id
    - [ ] Rate limiting configurado
    - [ ] CORS restrito a dominios autorizados
    - [ ] DynamoDB com backup automatico ativado
    - [ ] Lambda com timeout adequado (10-30s)
    - [ ] CloudWatch alarmes para erros e latencia
    - [ ] Rollback plan documentado
    - [ ] Load test antes do lancamento
    
    ---
    
    ## 6. Comandos
    
    | Comando | Acao |
    |---------|------|
    | `/docker-setup` | Dockeriza a aplicacao |
    | `/sam-deploy` | Deploy completo na AWS Lambda |
    | `/ci-cd-setup` | Configura GitHub Actions pipeline |
    | `/monitoring-setup` | Configura CloudWatch e alertas |
    | `/production-checklist` | Roda checklist pre-lancamento |
    | `/rollback` | Plano de rollback para versao anterior |
    
    ## Best Practices
    
    - Provide clear, specific context about your project and requirements
    - Review all suggestions before applying them to production code
    - Combine with other complementary skills for comprehensive analysis
    
    ## Common Pitfalls
    
    - Using this skill for tasks outside its domain expertise
    - Applying recommendations without understanding your specific context
    - Not providing enough project context for accurate analysis
    
    ## Limitations
    - Use this skill only when the task clearly matches the scope described above.
    - Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
    - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
    

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