deployment-engineer

charon-fan/agent-playbook · updated Apr 8, 2026

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$npx skills add https://github.com/charon-fan/agent-playbook --skill deployment-engineer
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summary

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

skill.md

Deployment Engineer

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

When This Skill Activates

Activates when you:

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

CI/CD Pipeline

Pipeline Stages

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

GitHub Actions Example

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

# Production
NODE_ENV=production
DATABASE_URL=postgresql://...
API_KEY=sk-...
SENTRY_DSN=https://example.com/123

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

Configuration Management

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

Health Checks

// 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

# 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

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

Scripts

Generate deployment config:

python scripts/generate_deploy.py <environment>

Validate deployment:

python scripts/validate_deploy.py

References

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

How to use deployment-engineer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add deployment-engineer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/charon-fan/agent-playbook --skill deployment-engineer

The skills CLI fetches deployment-engineer from GitHub repository charon-fan/agent-playbook and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/deployment-engineer

Reload or restart Cursor to activate deployment-engineer. Access the skill through slash commands (e.g., /deployment-engineer) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.860 reviews
  • Shikha Mishra· Dec 24, 2024

    deployment-engineer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ganesh Mohane· Dec 16, 2024

    Solid pick for teams standardizing on skills: deployment-engineer is focused, and the summary matches what you get after install.

  • Aisha Garcia· Dec 16, 2024

    deployment-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Evelyn Menon· Dec 8, 2024

    deployment-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Carlos Abebe· Dec 8, 2024

    Keeps context tight: deployment-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kwame Jackson· Dec 4, 2024

    Registry listing for deployment-engineer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Mateo Gill· Nov 27, 2024

    Useful defaults in deployment-engineer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sofia Bansal· Nov 27, 2024

    I recommend deployment-engineer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 7, 2024

    We added deployment-engineer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aditi Lopez· Nov 7, 2024

    deployment-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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