deployment-pipeline-design▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.
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
- Source - Code checkout
- Build - Compile, package, containerize
- Test - Unit, integration, security scans
- Staging Deploy - Deploy to staging environment
- Integration Tests - E2E, smoke tests
- Approval Gate - Manual approval required
- Production Deploy - Canary, blue-green, rolling
- Verification - Health checks, monitoring
- 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-designHow to use deployment-pipeline-design on Cursor
AI-first code editor with Composer
1Prerequisites
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
2Execute 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-designThe skills CLI fetches deployment-pipeline-design from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
3Select 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│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/deployment-pipeline-designReload 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.
Additional Resources
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.6★★★★★75 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.
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