Devops Pipeline logo

Devops Pipeline

Community
Omar-Obando
devops-pipeline

Use when designing and implementing CI/CD pipelines, deployment strategies, infrastructure as code, monitoring setup, and DevOps best practices. Includes GitHub Actions, Docker, Kubernetes, and deployment patterns. Based on Azure DevOps, CODING DevOps, and DevOps certification standards.

Overview

PublisherOmar-Obando
Repositoryqwen-orchestrator
Skill namedevops-pipeline
Stars
53
Forks
13
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Omar-Obando on GitHub. Read the source before you install it.

Installation

Install the Devops Pipeline 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.
    https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/devops-pipeline
  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/Omar-Obando/qwen-orchestrator.git /tmp/qwen-orchestrator
mkdir -p .claude/skills
cp -r /tmp/qwen-orchestrator/skills/devops-pipeline .claude/skills/devops-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Pipeline 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 Devops Pipeline 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 Devops Pipeline 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.

DevOps Pipeline Skill — CI/CD and Deployment Strategies

Overview

This skill provides comprehensive guidance for designing and implementing CI/CD pipelines, deployment strategies, infrastructure as code, monitoring setup, and DevOps best practices. It includes GitHub Actions, Docker, Kubernetes, and deployment patterns. Based on Azure DevOps, CODING DevOps, and DevOps certification standards.

When to Use

Use this skill when:

  • Designing CI/CD pipelines for any project
  • Setting up deployment strategies (blue-green, canary, rolling)
  • Configuring infrastructure as code (Terraform, CloudFormation)
  • Implementing monitoring and alerting (Prometheus, Grafana)
  • Optimizing deployment processes
  • Managing cloud infrastructure (AWS, Azure, GCP)
  • Setting up GitHub Actions workflows
  • Configuring Docker containers and images
  • Implementing Kubernetes deployments
  • Creating Helm charts for Kubernetes
  • Setting up container registries
  • Implementing container security scanning
  • Configuring CI/CD pipeline caching
  • Setting up pipeline secrets management
  • Implementing pipeline as code
  • Creating multi-stage pipeline strategies
  • Setting up deployment environments
  • Implementing rollback strategies
  • Creating deployment documentation
  • Building deployment monitoring dashboards
  • Setting up deployment alerting
  • Implementing deployment security gates
  • Creating deployment automation scripts

Do NOT use this skill when:

  • Writing application code (use developer skill or specific framework skill)
  • Database schema design (use database design skill)
  • UI/UX design (use frontend design skill)
  • Writing specific test cases (use testing-strategy skill)
  • Implementing business logic (use developer skill)

CI/CD Pipeline Stages

┌─────────────────────────────────────────────────────────────┐
│                  CI/CD Pipeline                              │
├─────────────────────────────────────────────────────────────┤
│  1. Checkout                                                 │
│  2. Install Dependencies                                     │
│  3. Lint & Format Check                                      │
│  4. Type Checking                                            │
│  5. Unit Tests                                               │
│  6. Integration Tests                                        │
│  7. Build                                                    │
│  8. Security Scan                                            │
│  9. E2E Tests (optional)                                     │
│  10. Deploy (Staging)                                        │
│  11. E2E Tests (Staging)                                     │
│  12. Deploy (Production)                                     │
└─────────────────────────────────────────────────────────────┘

GitHub Actions Pipeline

Complete CI/CD Pipeline

yaml
name: CI/CD Pipeline

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

env:
  NODE_VERSION: '18'
  NPM_VERSION: '9'

jobs:
  # Stage 1: Lint and Type Check
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: ${{ env.NODE_VERSION }}
      - run: npm ci
      - run: npm run lint
      - run: npm run typecheck

  # Stage 2: Unit Tests
  test-unit:
    runs-on: ubuntu-latest
    needs: lint
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: ${{ env.NODE_VERSION }}
      - run: npm ci
      - run: npm run test:unit
      - uses: codecov/codecov-action@v3
        with:
          files: ./coverage/lcov.info

  # Stage 3: Integration Tests
  test-integration:
    runs-on: ubuntu-latest
    needs: test-unit
    services:
      postgres:
        image: postgres:15
        env:
          POSTGRES_PASSWORD: test
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: ${{ env.NODE_VERSION }}
      - run: npm ci
      - run: npm run test:integration

  # Stage 4: Build
  build:
    runs-on: ubuntu-latest
    needs: test-integration
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: ${{ env.NODE_VERSION }}
      - run: npm ci
      - run: npm run build
      - uses: actions/upload-artifact@v3
        with:
          name: build-artifacts
          path: dist/

  # Stage 5: Deploy Staging
  deploy-staging:
    runs-on: ubuntu-latest
    needs: build
    if: github.ref == 'refs/heads/develop'
    environment: staging
    steps:
      - uses: actions/checkout@v3
      - run: npm ci
      - run: npm run build
      - run: npm run deploy:staging

  # Stage 6: E2E Staging
  test-e2e-staging:
    runs-on: ubuntu-latest
    needs: deploy-staging
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: ${{ env.NODE_VERSION }}
      - run: npm ci
      - run: npm run test:e2e -- --env=staging

  # Stage 7: Deploy Production
  deploy-production:
    runs-on: ubuntu-latest
    needs: test-e2e-staging
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v3
      - run: npm ci
      - run: npm run build
      - run: npm run deploy:production

Deployment Strategies

Blue-Green Deployment

┌─────────────────┐      ┌─────────────────┐
│  Blue (Current) │      │  Green (New)    │
│                 │      │                 │
│  v1.0.0         │─────►│  v1.1.0         │
│                 │      │                 │
└─────────────────┘      └─────────────────┘
      │                         │
      └──────────┬──────────────┘
                 │
           ┌─────▼─────┐
           │  Load     │
           │ Balancer  │
           └───────────┘

Canary Deployment

┌─────────────────┐
│  Main Service   │
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
┌───▼───┐ ┌───▼───┐
│ Blue  │ │Green  │
│ (90%) │ │(10%)  │
└───────┘ └───────┘

Rolling Update

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2
      maxUnavailable: 1
  template:
    spec:
      containers:
        - name: app
          image: myapp:2.0.0

Infrastructure as Code

Terraform Example

hcl
# main.tf
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

provider "aws" {
  region = var.aws_region
}

# VPC
resource "aws_vpc" "main" {
  cidr_block           = "10.0.0.0/16"
  enable_dns_hostnames = true
  enable_dns_support   = true

  tags = {
    Name        = "main-vpc"
    Environment = var.environment
  }
}

# EC2 Instance
resource "aws_instance" "app" {
  ami           = data.aws_ami.amazon_linux.id
  instance_type = var.instance_type

  vpc_security_group_ids = [aws_security_group.app.id]
  subnet_id              = aws_subnet.public.id

  tags = {
    Name        = "app-server"
    Environment = var.environment
  }
}

AWS CloudFormation Example

yaml
AWSTemplateFormatVersion: '2010-09-09'
Description: 'Simple EC2 instance with security group'

Resources:
  EC2Instance:
    Type: AWS::EC2::Instance
    Properties:
      ImageId: ami-0c55b159cbfafe1f0
      InstanceType: t2.micro
      SecurityGroupIds:
        - !Ref InstanceSecurityGroup
      Tags:
        - Key: Name
          Value: !Sub ${AWS::StackName}-instance

  InstanceSecurityGroup:
    Type: AWS::EC2::SecurityGroup
    Properties:
      GroupDescription: Enable SSH access
      VpcId: !Ref VPC
      SecurityGroupIngress:
        - IpProtocol: tcp
          FromPort: 22
          ToPort: 22
          CidrIp: 0.0.0.0/0

Monitoring and Alerting

Prometheus Configuration

yaml
# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

alerting:
  alertmanagers:
    - static_configs:
        - targets: ['alertmanager:9093']

rule_files:
  - /etc/prometheus/rules/*.yml

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'app'
    static_configs:
      - targets: ['app:8080']

Alert Rules

yaml
# rules/alerts.yml
groups:
  - name: application-alerts
    rules:
      - alert: HighErrorRate
        expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: 'High error rate detected'
          description: 'Error rate is above 5% for the last 5 minutes'

      - alert: HighLatency
        expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 1
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: 'High latency detected'
          description: '95th percentile latency is above 1 second'

Docker Best Practices

Multi-Stage Build

dockerfile
# Build stage
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

# Production stage
FROM node:18-alpine AS production
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY package*.json ./

EXPOSE 3000
CMD ["node", "dist/main.js"]

Docker Compose

yaml
version: '3.8'

services:
  app:
    build: .
    ports:
      - '3000:3000'
    environment:
      - NODE_ENV=production
      - DATABASE_URL=postgresql://postgres:postgres@db:5432/app
    depends_on:
      - db
    restart: unless-stopped

  db:
    image: postgres:15
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=app
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped

volumes:
  postgres_data:

Kubernetes Resources

Deployment

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
        - name: myapp
          image: myapp:1.0.0
          ports:
            - containerPort: 3000
          resources:
            requests:
              memory: '128Mi'
              cpu: '100m'
            limits:
              memory: '256Mi'
              cpu: '500m'
          livenessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 30
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /ready
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 5

Service

yaml
apiVersion: v1
kind: Service
metadata:
  name: myapp-service
spec:
  selector:
    app: myapp
  ports:
    - protocol: TCP
      port: 80
      targetPort: 3000
  type: LoadBalancer

Common Anti-Patterns

❌ Bad: No Pipeline Stages

yaml
# ❌ BAD: All in one job
jobs:
  build-and-deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - run: npm ci
      - run: npm run build
      - run: npm test
      - run: npm run deploy

Problems:

  • No parallelization
  • Hard to debug
  • No quality gates

✅ Good: Separate Stages

yaml
# ✅ GOOD: Separate stages
jobs:
  lint:
    runs-on: ubuntu-latest
    steps: [...]

  test:
    runs-on: ubuntu-latest
    needs: lint
    steps: [...]

  build:
    runs-on: ubuntu-latest
    needs: test
    steps: [...]

  deploy:
    runs-on: ubuntu-latest
    needs: build
    steps: [...]

❌ Bad: No Caching

yaml
# ❌ BAD: No caching
jobs:
  build:
    steps:
      - uses: actions/checkout@v3
      - run: npm ci # Always downloads
      - run: npm run build

✅ Good: With Caching

yaml
# ✅ GOOD: With caching
jobs:
  build:
    steps:
      - uses: actions/checkout@v3
      - uses: actions/cache@v3
        with:
          path: ~/.npm
          key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
          restore-keys: |
            ${{ runner.os }}-node-
      - run: npm ci
      - run: npm run build

Real-World Impact

Before this skill:

  • Manual deployments
  • Frequent failures
  • No testing gates
  • Difficult rollbacks

After this skill:

  • Automated CI/CD pipelines
  • Reliable deployments
  • Quality gates
  • Easy rollbacks

Cross-References

  • testing-strategy - For test coverage and quality gates
  • security-auditor - For security scans in pipeline
  • architecture-patterns - For scalable infrastructure design

References

Frequently asked questions

What does the Devops Pipeline AI skill do?

Use when designing and implementing CI/CD pipelines, deployment strategies, infrastructure as code, monitoring setup, and DevOps best practices. Includes GitHub Actions, Docker, Kubernetes, and deployment patterns. Based on Azure DevOps, CODING DevOps, and DevOps certification standards.

Why use Devops Pipeline on TypingMind?

Because you install it once and use it with any model. Devops Pipeline 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 Devops Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/devops-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Devops Pipeline?

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 Devops Pipeline?

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

Is the Devops Pipeline AI skill free?

Yes. It is published on GitHub by Omar-Obando 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇