New skills covering 10 categories: **Security & Audit**: 007 (STRIDE/PASTA/OWASP), cred-omega (secrets management) **AI Personas**: Karpathy, Hinton, Sutskever, LeCun (4 sub-skills), Altman, Musk, Gates, Jobs, Buffett **Multi-agent Orchestration**: agent-orchestrator, task-intelligence, multi-advisor **Code Analysis**: matematico-tao (Terence Tao-inspired mathematical code analysis) **Social & Messaging**: Instagram Graph API, Telegram Bot, WhatsApp Cloud API, social-orchestrator **Image Generation**: AI Studio (Gemini), Stability AI, ComfyUI Gateway, image-studio router **Brazilian Domain**: 6 auction specialist modules, 2 legal advisors, auctioneers data scraper **Product & Growth**: design, invention, monetization, analytics, growth engine **DevOps & LLM Ops**: Docker/CI-CD/AWS, RAG/embeddings/fine-tuning **Skill Governance**: installer, sentinel auditor, context management Each skill includes: - Standardized YAML frontmatter (name, description, risk, source, tags, tools) - Structured sections (Overview, When to Use, How it Works, Best Practices) - Python scripts and reference documentation where applicable - Cross-platform compatibility (Claude Code, Antigravity, Cursor, Gemini CLI, Codex CLI) Co-authored-by: ProgramadorBrasil <214873561+ProgramadorBrasil@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
295 lines
7.0 KiB
Markdown
295 lines
7.0 KiB
Markdown
---
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name: devops-deploy
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description: '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...'
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risk: critical
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source: community
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date_added: '2026-03-06'
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author: renat
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tags:
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- devops
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- docker
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- ci-cd
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- aws
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- terraform
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- github-actions
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tools:
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- claude-code
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- antigravity
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- cursor
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- gemini-cli
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- codex-cli
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---
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# DEVOPS-DEPLOY — Da Ideia para Producao
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## Overview
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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.
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## When to Use This Skill
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- When you need specialized assistance with this domain
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## Do Not Use This Skill When
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- The task is unrelated to devops deploy
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- A simpler, more specific tool can handle the request
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- The user needs general-purpose assistance without domain expertise
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## How It Works
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> "Move fast and don't break things." — Engenharia de elite nao e lenta.
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> E rapida e confiavel ao mesmo tempo.
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---
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## Dockerfile Otimizado (Python)
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```dockerfile
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FROM python:3.11-slim AS builder
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir --user -r requirements.txt
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FROM python:3.11-slim
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WORKDIR /app
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COPY --from=builder /root/.local /root/.local
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COPY . .
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ENV PATH=/root/.local/bin:$PATH
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ENV PYTHONUNBUFFERED=1
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EXPOSE 8000
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HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:8000/health || exit 1
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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```
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## Docker Compose (Dev Local)
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```yaml
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version: "3.9"
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services:
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app:
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build: .
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ports: ["8000:8000"]
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environment:
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- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
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volumes:
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- .:/app
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depends_on: [db, redis]
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db:
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image: postgres:15
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environment:
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POSTGRES_DB: auri
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POSTGRES_USER: auri
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POSTGRES_PASSWORD: ${DB_PASSWORD}
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volumes:
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- pgdata:/var/lib/postgresql/data
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redis:
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image: redis:7-alpine
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volumes:
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pgdata:
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```
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---
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## Sam Template (Serverless)
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```yaml
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## Template.Yaml
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AWSTemplateFormatVersion: '2010-09-09'
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Transform: AWS::Serverless-2016-10-31
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Globals:
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Function:
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Timeout: 30
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Runtime: python3.11
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Environment:
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Variables:
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ANTHROPIC_API_KEY: !Ref AnthropicApiKey
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DYNAMODB_TABLE: !Ref AuriTable
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Resources:
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AuriFunction:
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Type: AWS::Serverless::Function
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Properties:
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CodeUri: src/
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Handler: lambda_function.handler
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MemorySize: 512
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Policies:
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- DynamoDBCrudPolicy:
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TableName: !Ref AuriTable
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AuriTable:
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Type: AWS::DynamoDB::Table
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Properties:
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TableName: auri-users
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BillingMode: PAY_PER_REQUEST
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AttributeDefinitions:
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- AttributeName: userId
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AttributeType: S
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KeySchema:
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- AttributeName: userId
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KeyType: HASH
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TimeToLiveSpecification:
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AttributeName: ttl
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Enabled: true
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```
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## Deploy Commands
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```bash
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## Build E Deploy
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sam build
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sam deploy --guided # primeira vez
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sam deploy # deploys seguintes
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## Deploy Rapido (Sem Confirmacao)
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sam deploy --no-confirm-changeset --no-fail-on-empty-changeset
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## Ver Logs Em Tempo Real
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sam logs -n AuriFunction --tail
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## Deletar Stack
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sam delete
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```
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---
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## .Github/Workflows/Deploy.Yml
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name: Deploy Auri
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on:
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push:
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branches: [main]
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pull_request:
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branches: [main]
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with: { python-version: "3.11" }
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- run: pip install -r requirements.txt
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- run: pytest tests/ -v --cov=src --cov-report=xml
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- uses: codecov/codecov-action@v4
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security:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- run: pip install bandit safety
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- run: bandit -r src/ -ll
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- run: safety check -r requirements.txt
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deploy:
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needs: [test, security]
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if: github.ref == 'refs/heads/main'
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: aws-actions/setup-sam@v2
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- uses: aws-actions/configure-aws-credentials@v4
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with:
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aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
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aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
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aws-region: us-east-1
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- run: sam build
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- run: sam deploy --no-confirm-changeset
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- name: Notify Telegram on Success
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run: |
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curl -s -X POST "https://api.telegram.org/bot${{ secrets.TELEGRAM_BOT_TOKEN }}/sendMessage" \
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-d "chat_id=${{ secrets.TELEGRAM_CHAT_ID }}" \
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-d "text=Auri deployed successfully! Commit: ${{ github.sha }}"
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```
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---
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## Health Check Endpoint
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```python
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from fastapi import FastAPI
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import time, os
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app = FastAPI()
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START_TIME = time.time()
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@app.get("/health")
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async def health():
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return {
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"status": "healthy",
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"uptime_seconds": time.time() - START_TIME,
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"version": os.environ.get("APP_VERSION", "unknown"),
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"environment": os.environ.get("ENV", "production")
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}
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```
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## Alertas Cloudwatch
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```python
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import boto3
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def create_error_alarm(function_name: str, sns_topic_arn: str):
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cw = boto3.client("cloudwatch")
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cw.put_metric_alarm(
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AlarmName=f"{function_name}-errors",
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MetricName="Errors",
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Namespace="AWS/Lambda",
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Dimensions=[{"Name": "FunctionName", "Value": function_name}],
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Period=300,
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EvaluationPeriods=1,
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Threshold=5,
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ComparisonOperator="GreaterThanThreshold",
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AlarmActions=[sns_topic_arn],
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TreatMissingData="notBreaching"
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)
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```
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---
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## 5. Checklist De Producao
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- [ ] Variaveis de ambiente via Secrets Manager (nunca hardcoded)
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- [ ] Health check endpoint respondendo
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- [ ] Logs estruturados (JSON) com request_id
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- [ ] Rate limiting configurado
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- [ ] CORS restrito a dominios autorizados
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- [ ] DynamoDB com backup automatico ativado
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- [ ] Lambda com timeout adequado (10-30s)
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- [ ] CloudWatch alarmes para erros e latencia
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- [ ] Rollback plan documentado
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- [ ] Load test antes do lancamento
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---
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## 6. Comandos
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| Comando | Acao |
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| `/docker-setup` | Dockeriza a aplicacao |
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| `/sam-deploy` | Deploy completo na AWS Lambda |
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| `/ci-cd-setup` | Configura GitHub Actions pipeline |
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| `/monitoring-setup` | Configura CloudWatch e alertas |
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| `/production-checklist` | Roda checklist pre-lancamento |
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| `/rollback` | Plano de rollback para versao anterior |
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## Best Practices
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- Provide clear, specific context about your project and requirements
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- Review all suggestions before applying them to production code
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- Combine with other complementary skills for comprehensive analysis
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## Common Pitfalls
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- Using this skill for tasks outside its domain expertise
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- Applying recommendations without understanding your specific context
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- Not providing enough project context for accurate analysis
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