Imported from sickn33/agentic-awesome-skills (skills/devops-deploy/SKILL.md). Install upstream with npx skills add sickn33/agentic-awesome-skills --skill devops-deploy. Copyright stays with the author.

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

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.