semantic-versioning
>
Works with
---
name: semantic-versioning
description: >
license: MIT
---
# Semantic Versioning
## Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
## Overview
Establish semantic versioning practices to maintain consistent version numbering aligned with release significance, enabling automated version management and release notes generation.
## When to Use
- Package and library releases
- API versioning
- Version bumping automation
- Release note generation
- Breaking change tracking
- Dependency management
- Changelog management
## Quick Start
Minimal working example:
```yaml
# package.json
{
"name": "my-awesome-package",
"version": "1.2.3",
"description": "An awesome package",
"main": "dist/index.js",
"repository": { "type": "git", "url": "https://github.com/org/repo.git" },
"scripts": { "release": "semantic-release" },
"devDependencies":
{
"semantic-release": "^21.0.0",
"@semantic-release/changelog": "^6.0.0",
"@semantic-release/git": "^10.0.0",
"@semantic-release/github": "^9.0.0",
"conventional-changelog-cli": "^3.0.0",
},
}
```
## Reference Guides
Detailed implementations in the `references/` directory:
| Guide | Contents |
|---|---|
| [Semantic Versioning Configuration](references/semantic-versioning-configuration.md) | Semantic Versioning Configuration |
| [Conventional Commits Format](references/conventional-commits-format.md) | Conventional Commits Format |
| [Semantic Release Configuration](references/semantic-release-configuration.md) | Semantic Release Configuration |
| [Version Bumping Script](references/version-bumping-script.md) | Version Bumping Script |
| [Changelog Generation](references/changelog-generation.md) | Changelog Generation |
## Best Practices
### ✅ DO
- Follow strict MAJOR.MINOR.PATCH format
- Use conventional commits
- Automate version bumping
- Generate changelogs automatically
- Tag releases in git
- Document breaking changes
- Use prerelease versions for testing
### ❌ DON'T
- Manually bump versions inconsistently
- Skip breaking change documentation
- Use arbitrary version numbering
- Mix features in patch releasesMore 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).
