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Deployment Engineer

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zhaono1
deployment-engineer

Deployment automation specialist for CI/CD pipelines and infrastructure. Use when setting up deployment, configuring CI/CD, or managing releases.

Overview

Publisherzhaono1
Repositoryagent-playbook
Skill namedeployment-engineer
Stars
79
Forks
12
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhaono1 on GitHub. Read the source before you install it.

Installation

Install the Deployment Engineer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhaono1/agent-playbook.git /tmp/agent-playbook
mkdir -p .claude/skills
cp -r /tmp/agent-playbook/skills/deployment-engineer .claude/skills/deployment-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deployment Engineer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deployment Engineer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deployment Engineer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deployment Engineer

Specialist in deployment automation, CI/CD pipelines, and infrastructure management.

Permission Boundary

Planning, generating configuration, and running read-only validation do not authorize deployment. Never push, deploy, roll back, change production infrastructure, or alter remote release state without explicit user approval for that exact action and target. Before an approved mutation, state the environment, command, expected impact, verification signal, and rollback path. Stop if the target or authority is ambiguous.

When This Skill Activates

Activates when you:

  • Set up deployment pipeline
  • Configure CI/CD
  • Manage releases
  • Automate infrastructure

CI/CD Pipeline

Pipeline Stages

yaml
stages:
  - lint
  - test
  - build
  - security
  - deploy-dev
  - deploy-staging
  - deploy-production

GitHub Actions Example

yaml
name: CI/CD

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '20'
      - run: npm ci
      - run: npm run lint

  test:
    runs-on: ubuntu-latest
    needs: lint
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
      - run: npm ci
      - run: npm test

  build:
    runs-on: ubuntu-latest
    needs: test
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
      - run: npm ci
      - run: npm run build
      - uses: actions/upload-artifact@v4
        with:
          name: build
          path: dist/

  deploy-production:
    runs-on: ubuntu-latest
    needs: build
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      - uses: actions/download-artifact@v4
        with:
          name: build
          path: dist/
      - run: npm run deploy

Deployment Strategies

1. Blue-Green Deployment

         ┌─────────┐
         │  Load   │
         │ Balancer│
         └────┬────┘
     ┌────────┴────────┐
     │    Switch       │
     ├────────┬────────┤
     ▼        ▼        ▼
  ┌─────┐ ┌─────┐ ┌─────┐
  │Blue │ │Green│ │     │
  └─────┘ └─────┘ └─────┘

2. Rolling Deployment

┌─────────────────────────────────────┐
│ v1  v1  v1  v1  v1  v1  v1  v1  v1 │ → Old
│ v2  v2  v2  v2  v2  v2  v2  v2  v2 │ → New
└─────────────────────────────────────┘
    ▲                       ▲
    │                       │
  Start                  End

3. Canary Deployment

┌──────────────────────────────────────┐
│ v1  v1  v1  v1  v1  v1  v1  v1  v1  v1 │ → Old
│ v2  v2  v2  v2                        │ → Canary (5%)
└──────────────────────────────────────┘

Monitor metrics, then:
│ v1  v1  v1  v1                        │ → Old (50%)
│ v2  v2  v2  v2  v2  v2  v2  v2  v2  v2 │ → New (50%)

Environment Configuration

Environment Variables

bash
# Production
NODE_ENV=production
DATABASE_URL=postgresql://...
API_KEY=${API_KEY}
SENTRY_DSN=https://example.com/123

# Development
NODE_ENV=development
DATABASE_URL=postgresql://localhost:5432/dev

Configuration Management

typescript
// config/production.ts
export default {
  database: {
    url: process.env.DATABASE_URL,
    poolSize: 20,
  },
  redis: {
    url: process.env.REDIS_URL,
  },
};

Health Checks

typescript
// GET /health
app.get('/health', (req, res) => {
  const health = {
    status: 'ok',
    timestamp: new Date().toISOString(),
    checks: {
      database: 'ok',
      redis: 'ok',
      external_api: 'ok',
    },
  };

  if (Object.values(health.checks).some(v => v !== 'ok')) {
    health.status = 'degraded';
    return res.status(503).json(health);
  }

  res.json(health);
});

Rollback Strategy

bash
# Kubernetes
kubectl rollout undo deployment/app

# Docker
docker-compose down
docker-compose up -d --scale app=<previous-version>

# Git
git revert HEAD
git push

Monitoring & Logging

Metrics to Track

  • Deployment frequency
  • Lead time for changes
  • Mean time to recovery (MTTR)
  • Change failure rate

Logging

typescript
// Structured logging
logger.info('Deployment started', {
  version: process.env.VERSION,
  environment: process.env.NODE_ENV,
  timestamp: new Date().toISOString(),
});

Scripts

Generate deployment config:

bash
python3 scripts/generate_deploy.py --env <environment> --name <service-name>

Validate deployment:

bash
python3 scripts/validate_deploy.py --input deploy-plan.md

References

  • references/pipelines.md - CI/CD pipeline examples
  • references/kubernetes.md - K8s deployment configs
  • references/monitoring.md - Monitoring setup

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deployment Engineer AI skill do?

Deployment automation specialist for CI/CD pipelines and infrastructure. Use when setting up deployment, configuring CI/CD, or managing releases.

Why use Deployment Engineer on TypingMind?

Because you install it once and use it with any model. Deployment Engineer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deployment Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaono1/agent-playbook/tree/main/skills/deployment-engineer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deployment Engineer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deployment Engineer?

As many as you like. As long as a model supports skills, you can use Deployment Engineer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deployment Engineer AI skill free?

Yes. It is published on GitHub by zhaono1 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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