cicd-pipeline-setup
>
Works with
---
name: cicd-pipeline-setup
description: >
license: MIT
---
# CI/CD Pipeline Setup
## Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
## Overview
Build automated continuous integration and deployment pipelines that test code, build artifacts, run security checks, and deploy to multiple environments with minimal manual intervention.
## When to Use
- Automated code testing and quality checks
- Containerized application builds
- Multi-environment deployments
- Release management and versioning
- Automated security scanning
- Performance testing integration
- Artifact management and registry
## Quick Start
Minimal working example:
```yaml
# .github/workflows/deploy.yml
name: Build and Deploy
on:
push:
branches:
- main
- develop
pull_request:
branches:
- main
workflow_dispatch:
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
node-version: [18.x, 20.x]
steps:
// ... (see reference guides for full implementation)
```
## Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents |
|---|---|
| [GitHub Actions Workflow](references/github-actions-workflow.md) | GitHub Actions Workflow |
| [GitLab CI Pipeline](references/gitlab-ci-pipeline.md) | GitLab CI Pipeline |
| [Jenkins Pipeline](references/jenkins-pipeline.md) | Jenkins Pipeline |
| [CI/CD Script](references/cicd-script.md) | CI/CD Script |
## Best Practices
### ✅ DO
- Fail fast with early validation
- Run tests in parallel when possible
- Use caching for dependencies
- Implement proper secret management
- Gate production deployments with approval
- Monitor and alert on pipeline failures
- Use consistent environment configuration
- Implement infrastructure as code
### ❌ DON'T
- Store credentials in pipeline configuration
- Deploy without automated tests
- Skip security scanning
- Allow long-running pipelines
- Mix staging and production pipelines
- Ignore test failures
- Deploy directly to main branch
- Skip health checks after deploymentMore Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

