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build-%26-deploybuild 26 部署

Agent Skill

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

总安装

4,060

周安装

203

GitHub Stars

4

下载量

2,459
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:build-%26-deploy(build 26 部署)
来源仓库:https://github.com/eyadsibai/ltk
仓库路径:skills/build-%26-deploy
安装命令:
npx skills add https://github.com/eyadsibai/ltk --skill 'Build & Deploy'
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/eyadsibai/ltk --skill 'Build & Deploy'

简介

build-%26-deploy 用于辅助云部署、容器化和基础设施管理,提供构建验证与发布策略支持。

  • 它涵盖预构建检查、环境变量设置、测试运行及部署后验证,确保流程安全可靠。
  • 使用时需明确目标环境、账号权限和资源组,区分本地测试与生产操作边界。
  • 安装前请确认仓库权限、维护状态,以及是否会删除资源、重启服务或修改网络配置。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Build & Deploy

Comprehensive build and deployment skill for validation, CI/CD patterns, and deployment strategies.

Core Capabilities

Build Validation

Validate builds before deployment:

Pre-build checks:

  • Dependencies installed correctly
  • Environment variables set
  • Required services available
  • Configuration files valid

Build process:

# Python project
pip install -r requirements.txt
python -m pytest
python -m mypy src/
python -m build

# Node.js project
npm ci
npm run lint
npm run test
npm run build

Post-build validation:

  • Build artifacts exist
  • Artifact sizes reasonable
  • Version numbers correct
  • No development dependencies in production

CI/CD Patterns

Common continuous integration/deployment patterns:

GitHub Actions:

name: CI/CD Pipeline

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 --cov=src

  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: docker build -t app:${{ github.sha }} .

  deploy:
    needs: build
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
      - run: echo "Deploy to production"

Pipeline stages:

  1. Lint: Code style and quality
  2. Test: Unit and integration tests
  3. Build: Create artifacts
  4. Security: Scan for vulnerabilities
  5. Deploy: Push to environment

Deployment Strategies

Choose appropriate deployment approach:

Rolling Deployment:

  • Gradual replacement of instances
  • Zero downtime
  • Easy rollback
  • Best for: Stateless services

Blue-Green Deployment:

  • Two identical environments
  • Instant switch between versions
  • Simple rollback
  • Best for: Critical services

Canary Deployment:

  • Small percentage gets new version
  • Gradual traffic increase
  • Risk mitigation
  • Best for: High-traffic services

Feature Flags:

  • Deploy code, enable separately
  • Gradual rollout to users
  • Quick disable if issues
  • Best for: New features

Pre-flight Checks

Validation before deployment:

Checklist:

[ ] All tests pass
[ ] Security scan clean
[ ] Build artifacts valid
[ ] Configuration correct
[ ] Database migrations ready
[ ] Dependencies compatible
[ ] Rollback plan documented
[ ] Monitoring configured
[ ] Team notified

Automated checks:

# Environment validation
./scripts/check-env.sh

# Database connectivity
./scripts/check-db.sh

# External service health
./scripts/check-services.sh

# Configuration validation
./scripts/validate-config.sh

Build Workflows

Local Build

For development and testing:

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Build package
python -m build

Production Build

For deployment:

# Install production dependencies only
pip install -r requirements.txt

# Run production build
python -m build --wheel

# Verify artifact
ls dist/

Container Build

For containerized deployments:

# Multi-stage Dockerfile
FROM python:3.11-slim as builder

WORKDIR /app
COPY requirements.txt .
RUN pip wheel --no-cache-dir --wheel-dir /wheels -r requirements.txt

FROM python:3.11-slim
WORKDIR /app
COPY --from=builder /wheels /wheels
RUN pip install --no-cache /wheels/*
COPY src/ ./src/
CMD ["python", "-m", "src.main"]

Deployment Workflows

GCP Cloud Run

# Build and push image
gcloud builds submit --tag gcr.io/PROJECT/APP

# Deploy to Cloud Run
gcloud run deploy APP \
  --image gcr.io/PROJECT/APP \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

GCP Compute Engine

# Create instance template
gcloud compute instance-templates create APP-template \
  --machine-type=e2-medium \
  --image-family=debian-11 \
  --metadata-from-file=startup-script=startup.sh

# Update managed instance group
gcloud compute instance-groups managed rolling-action start-update APP-group \
  --version=template=APP-template

GCP Cloud Functions

# Deploy function
gcloud functions deploy FUNCTION_NAME \
  --runtime python311 \
  --trigger-http \
  --entry-point main \
  --source ./src

Environment Management

Environment Variables

Required variables:

# Application
APP_ENV=production
APP_DEBUG=false
APP_SECRET_KEY=<secret>

# Database
DATABASE_URL=postgresql://...
REDIS_URL=redis://...

# External Services
API_KEY=<key>

Validation:

required_vars = [
    'DATABASE_URL',
    'APP_SECRET_KEY',
    'API_KEY',
]

missing = [v for v in required_vars if not os.getenv(v)]
if missing:
    raise ValueError(f"Missing env vars: {missing}")

Configuration Management

Environment-specific configs:

config/
├── base.py       # Shared settings
├── development.py
├── staging.py
└── production.py

Loading pattern:

import os
env = os.getenv('APP_ENV', 'development')
config = importlib.import_module(f'config.{env}')

Rollback Procedures

Quick Rollback

# GCP Cloud Run
gcloud run services update-traffic APP \
  --to-revisions=PREVIOUS_REVISION=100

# Docker/Kubernetes
kubectl rollout undo deployment/APP

# Database (if migration failed)
python manage.py migrate APP PREVIOUS_MIGRATION

Rollback Checklist

[ ] Identify the issue
[ ] Notify stakeholders
[ ] Execute rollback command
[ ] Verify service health
[ ] Investigate root cause
[ ] Document incident

Monitoring Integration

Health Checks

@app.get("/health")
def health_check():
    return {
        "status": "healthy",
        "version": APP_VERSION,
        "timestamp": datetime.utcnow().isoformat()
    }

@app.get("/ready")
def readiness_check():
    # Check dependencies
    db_ok = check_database()
    cache_ok = check_redis()
    return {
        "ready": db_ok and cache_ok,
        "checks": {
            "database": db_ok,
            "cache": cache_ok
        }
    }

Deployment Metrics

Track after deployment:

  • Response times
  • Error rates
  • Resource utilization
  • Business metrics

Integration

Coordinate with other skills:

  • security-scanning skill: Pre-deploy security checks
  • test-coverage skill: Ensure adequate coverage
  • git-workflows skill: Tag releases, update changelog

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.84%
按下载量换算881

Claude

28.54%
按下载量换算702

Cursor

18.78%
按下载量换算462

Gemini CLI

8.55%
按下载量换算210

安全审计

暂无安全审计结果可展示。

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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