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- thiagofernandes1987-create/APEX
- 최근 소스 활동
- 2026년 7월 21일 11:53
- 감지된 SKILL.md 언어
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/thiagofernandes1987-create/APEX --skill devops-deploy명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
SOC 직업 분류 기준
SKILL.md 표시 중
| name | devops-deploy |
| description | "When you need specialized assistance with this domain" |
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.
"Move fast and don't break things." — Engenharia de elite nao e lenta. E rapida e confiavel ao mesmo tempo.
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:$PATH
ENV PYTHONUNBUFFERED=1
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:8000/health || exit 1
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
version: "3.9"
services:
app:
build: .
ports: ["8000:8000"]
environment:
- ANTHROPIC_API_KEY=${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:
## 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:
-
## 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
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")
}
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"
)
| 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 |
Implement —