deployment-pipeline-design

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill deployment-pipeline-design
0 commentsdiscussion
summary

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

skill.md

Deployment Pipeline Design

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

Do not use this skill when

  • The task is unrelated to deployment pipeline design
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Purpose

Design robust, secure deployment pipelines that balance speed with safety through proper stage organization and approval workflows.

Use this skill when

  • Design CI/CD architecture
  • Implement deployment gates
  • Configure multi-environment pipelines
  • Establish deployment best practices
  • Implement progressive delivery

Pipeline Stages

Standard Pipeline Flow

┌─────────┐   ┌──────┐   ┌─────────┐   ┌────────┐   ┌──────────┐
│  Build  │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│
└─────────┘   └──────┘   └─────────┘   └────────┘   └──────────┘

Detailed Stage Breakdown

  1. Source - Code checkout
  2. Build - Compile, package, containerize
  3. Test - Unit, integration, security scans
  4. Staging Deploy - Deploy to staging environment
  5. Integration Tests - E2E, smoke tests
  6. Approval Gate - Manual approval required
  7. Production Deploy - Canary, blue-green, rolling
  8. Verification - Health checks, monitoring
  9. Rollback - Automated rollback on failure

Approval Gate Patterns

Pattern 1: Manual Approval

# GitHub Actions
production-deploy:
  needs: staging-deploy
  environment:
    name: production
    url: https://app.example.com
  runs-on: ubuntu-latest
  steps:
    - name: Deploy to production
      run: |
        # Deployment commands

Pattern 2: Time-Based Approval

# GitLab CI
deploy:production:
  stage: deploy
  script:
    - deploy.sh production
  environment:
    name: production
  when: delayed
  start_in: 30 minutes
  only:
    - main

Pattern 3: Multi-Approver

# Azure Pipelines
stages:
- stage: Production
  dependsOn: Staging
  jobs:
  - deployment: Deploy
    environment:
      name: production
      resourceType: Kubernetes
    strategy:
      runOnce:
        preDeploy:
          steps:
          - task: ManualValidation@0
            inputs:
              notifyUsers: '[email protected]'
              instructions: 'Review staging metrics before approving'

Reference: See assets/approval-gate-template.yml

Deployment Strategies

1. Rolling Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2
      maxUnavailable: 1

Characteristics:

  • Gradual rollout
  • Zero downtime
  • Easy rollback
  • Best for most applications

2. Blue-Green Deployment

# Blue (current)
kubectl apply -f blue-deployment.yaml
kubectl label service my-app version=blue

# Green (new)
kubectl apply -f green-deployment.yaml
# Test green environment
kubectl label service my-app version=green

# Rollback if needed
kubectl label service my-app version=blue

Characteristics:

  • Instant switchover
  • Easy rollback
  • Doubles infrastructure cost temporarily
  • Good for high-risk deployments

3. Canary Deployment

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    canary:
      steps:
      - setWeight: 10
      - pause: {duration: 5m}
      - setWeight: 25
      - pause: {duration: 5m}
      - setWeight: 50
      - pause: {duration: 5m}
      - setWeight: 100

Characteristics:

  • Gradual traffic shift
  • Risk mitigation
  • Real user testing
  • Requires service mesh or similar

4. Feature Flags

from flagsmith import Flagsmith

flagsmith = Flagsmith(environment_key="API_KEY")

if flagsmith.has_feature("new_checkout_flow"):
    # New code path
    process_checkout_v2()
else:
    # Existing code path
    process_checkout_v1()

Characteristics:

  • Deploy without releasing
  • A/B testing
  • Instant rollback
  • Granular control

Pipeline Orchestration

Multi-Stage Pipeline Example

name: Production Pipeline

on:
  push:
    branches: [ main ]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build application
        run: make build
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      - name: Push to registry
        run: docker push myapp:${{ github.sha }}

  test:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Unit tests
        run: make test
      - name: Security scan
        run: trivy image myapp:${{ github.sha }}

  deploy-staging:
    needs: test
    runs-on: ubuntu-latest
    environment:
      name: staging
    steps:
      - name: Deploy to staging
        run: kubectl apply -f k8s/staging/

  integration-test:
    needs: deploy-staging
    runs-on: ubuntu-latest
    steps:
      - name: Run E2E tests
        run: npm run test:e2e

  deploy-production:
    needs: integration-test
    runs-on: ubuntu-latest
    environment:
      name: production
    steps:
      - name: Canary deployment
        run: |
          kubectl apply -f k8s/production/
          kubectl argo rollouts promote my-app

  verify:
    
how to use deployment-pipeline-design

How to use deployment-pipeline-design 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-pipeline-design
2

Execute installation command

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

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill deployment-pipeline-design

The skills CLI fetches deployment-pipeline-design from GitHub repository sickn33/antigravity-awesome-skills 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-pipeline-design

Reload or restart Cursor to activate deployment-pipeline-design. Access the skill through slash commands (e.g., /deployment-pipeline-design) 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

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.675 reviews
  • Pratham Ware· Dec 28, 2024

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

  • Aanya Kim· Dec 20, 2024

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

  • Kabir Okafor· Dec 20, 2024

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

  • Harper Zhang· Dec 12, 2024

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

  • Chen Martinez· Dec 8, 2024

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

  • Aditi Bhatia· Nov 27, 2024

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

  • Yash Thakker· Nov 19, 2024

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

  • Isabella Gupta· Nov 11, 2024

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

  • Kabir Nasser· Nov 11, 2024

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

  • Harper Smith· Nov 3, 2024

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

showing 1-10 of 75

1 / 8