Deploy Native Skill

What it does

When your agent uses it

• Tasks that involve Linux administration
• Tasks that involve Backend development

Example prompts

• “/deploy-native-skill”

Requirements

• Python 3
• Node.js
• A Bash shell
• PowerShell
• Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1读取 deploy-profile.md
2. 2选择进程管理方式
3. 3生成部署脚本骨架
4. 4按 OS 输出
5. 5输出使用说明

What it can do on your machine

Read from SKILL.md and the folder at commit a4a640b. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (JavaScript, PowerShell and Shell), which the agent can run.

  Shell commands in SKILL.md call:

  • git
  • node
  • npm
  • uvicorn
  • gunicorn
  • java
  • go
  • bundle

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use git and npm, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deploy Native Skill loads about 1.6k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 326 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~55

When it runs · the whole SKILL.md, loaded when a task matches

~1.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:165

  ets/my-api.service`，端口经 `EnvironmentFile=.env` 中的 `APP_PORT` 注入。

• NoteMentions a .env fileSKILL.md:176

  EnvironmentFile=/srv/apps/my-api/.env

• NoteRuns commands with sudoSKILL.md:190

  sudo cp deploy/my-api.service /etc/systemd/system/

• NoteRuns commands with sudoSKILL.md:191

  sudo systemctl daemon-reload

• NoteRuns commands with sudoSKILL.md:192

  sudo systemctl enable --now my-api

• NoteMentions a .env fileSKILL.md:228

  DIR}/launch.log`）、端口统一读 `APP_PORT`（命令行 > .env > 默认 8080）、优雅停老进程、健康检查重试 10 次、失败非零退出。

• NoteMentions a .env fileSKILL.md:244

  - 不在脚本里硬编码密钥；从 `.env` 读取。

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jiushiwon/wg-skills at commit a4a640b, republished under its Apache-2.0 licence (© jiushiwon). 326 words, ~1,604 tokens.

name

deploy-native-skill

description

用于按 deploy-profile.md 生成原生（非 Docker）部署脚本，覆盖 git 拉取、依赖安装、构建、优雅停止老进程、后台启动新进程、日志定向、健康检查等步骤，按 OS 输出 .sh 或 .ps1。支持 systemd / pm2 / supervisor / nohup 等进程管理方式。当用户说「原生部署」「deploy native」「生成部署脚本」「一键部署脚本」时触发。

Phase 1: 读取 deploy-profile.md
  → 提取语言、框架、数据库、缓存、构建产物
  → 确认是否含前端产物（是则联动 static-nginx-skill）

Phase 2: 选择进程管理方式
  → Linux：systemd（首选）/ pm2（Node）/ supervisor / nohup
  → Windows：NSSM / Task Scheduler / PowerShell 后台 job

Phase 3: 生成部署脚本骨架
  → 拉代码（git pull / fetch+reset）
  → 装依赖（npm install / pip install / go mod download / mvn package）
  → 构建（如有）
  → 优雅停老进程（按端口或 PID 文件）
  → 后台起新进程
  → 日志定向到指定目录
  → 健康检查（重试 N 次）

Phase 4: 按 OS 输出
  → Linux/macOS：deploy.sh（bash）
  → Windows：deploy.ps1（PowerShell）

Phase 5: 输出使用说明
  → 首次配置（systemd unit / pm2 ecosystem）
  → 日志位置、回滚方式

#!/usr/bin/env bash
set -euo pipefail

APP_NAME="my-api"
APP_DIR="/srv/apps/my-api"
LOG_DIR="/var/log/${APP_NAME}"
PORT=8080
BRANCH="main"

mkdir -p "$LOG_DIR"
cd "$APP_DIR"

echo "[1/6] pull latest code"
git fetch origin "$BRANCH"
git reset --hard "origin/$BRANCH"

echo "[2/6] install deps"
npm ci --production

echo "[3/6] build"
npm run build

echo "[4/6] stop old process"
if command -v pm2 >/dev/null 2>&1; then
  pm2 stop "$APP_NAME" || true
else
  lsof -ti:"$PORT" | xargs -r kill -TERM || true
  sleep 2
  lsof -ti:"$PORT" | xargs -r kill -KILL || true
fi

echo "[5/6] start new process"
if command -v pm2 >/dev/null 2>&1; then
  pm2 start ecosystem.config.js --env production
  pm2 save
else
  nohup node dist/server.js >> "${LOG_DIR}/app.log" 2>&1 &
  echo $! > "${APP_DIR}/${APP_NAME}.pid"
fi

echo "[6/6] health check"
for i in $(seq 1 10); do
  if curl -fsS "http://127.0.0.1:${PORT}/health" >/dev/null; then
    echo "deploy OK"
    exit 0
  fi
  sleep 2
done
echo "health check failed" >&2
exit 1

[Unit]
Description=My API Service
After=network.target

[Service]
Type=simple
User=www-data
WorkingDirectory=/srv/apps/my-api
EnvironmentFile=/srv/apps/my-api/.env
ExecStart=/usr/bin/node /srv/apps/my-api/dist/server.js
Restart=always
RestartSec=5
StandardOutput=append:/var/log/my-api/app.log
StandardError=append:/var/log/my-api/app.log

[Install]
WantedBy=multi-user.target

sudo cp deploy/my-api.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable --now my-api

module.exports = {
  apps: [{
    name: 'my-api',
    script: 'dist/server.js',
    instances: 'max',
    exec_mode: 'cluster',
    env_production: {
      NODE_ENV: 'production',
      APP_PORT: 8080
    },
    error_file: '/var/log/my-api/err.log',
    out_file: '/var/log/my-api/out.log',
    merge_logs: true
  }]
}

© jiushiwon, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references, assets) in vibeCoding/super-deploy-skills/deploy-native-skill of jiushiwon/wg-skills.

• SKILL.md
• README.md
• assets/ecosystem.config.js
• assets/launch.ps1
• assets/launch.sh
• assets/my-api.service
• references/script-pipeline.md
• references/script-standards.md

Open the folder on GitHubat commit a4a640b