creating-changesets
Creates changesets for semantic versioning. Use when adding changesets, preparing releases, determining version bumps (patch/minor/major), generating changelog entries, or documenting breaking changes.
Works with
---
name: creating-changesets
description: Creates changesets for semantic versioning. Use when adding changesets, preparing releases, determining version bumps (patch/minor/major), generating changelog entries, or documenting breaking changes.
license: BSD-3-Clause
---
# Changeset & Release Manager
## Overview
Automate the creation of changesets following project conventions, ensuring proper version bumps and well-documented release notes.
## When to Use
- After completing a feature or fix
- Before creating a PR
- When preparing a release
- To document breaking changes
## What is a Changeset?
A changeset is a markdown file in the `.changeset/` directory that describes:
1. Which packages are affected
2. What type of version bump (patch/minor/major)
3. A description of the change
## Changeset Types
| Type | When to Use | Version Change |
|------|-------------|----------------|
| `patch` | Bug fixes, documentation, refactoring, dependency updates | 1.0.0 → 1.0.1 |
| `minor` | New features, non-breaking enhancements | 1.0.0 → 1.1.0 |
| `major` | Breaking changes, API modifications | 1.0.0 → 2.0.0 |
## Decision Guide
### Use `patch` for:
- Bug fixes that don't change behavior
- Documentation updates
- Internal refactoring (no API changes)
- Dependency updates (non-breaking)
- Performance improvements
- Code style/linting fixes
### Use `minor` for:
- New features
- New CLI commands
- New configuration options
- Enhanced functionality
- New entity types support
- Non-breaking API additions
### Use `major` for:
- Breaking configuration changes
- Removed features or commands
- Changed CLI interface
- Required migration steps
- Node.js version requirement changes
## Creating a Changeset
### Interactive Method
```bash
pnpm changeset
```
Follow the prompts:
1. Select affected packages (space to select)
2. Choose bump type for each package
3. Write a summary of changes
### Manual Method
Create a file in `.changeset/` with a random name:
```markdown
---
"@saleor/configurator": minor
---
Add support for reference attributes with entityType field
- Attributes of type REFERENCE now require an entityType field
- Introspection properly captures entity type references
- Deploy correctly handles reference attribute creation
```
### File Format
```markdown
---
"package-name": patch|minor|major
---
Short description of the change (shown in CHANGELOG)
Optional longer description with:
- Bullet points for details
- Code examples if needed
- Migration instructions for breaking changes
```
## Release Workflow
### 1. Create Changeset
```bash
pnpm changeset
git add .changeset/
git commit -m "chore: add changeset for feature"
```
### 2. PR and Review
- Changeset is part of the PR
- Reviewers can suggest bump type changes
### 3. Merge to Main
- Changesets action creates "Version Packages" PR
- This PR updates version and CHANGELOG
### 4. Merge Version PR
- Triggers npm publish
- Creates GitHub release
## Checking Status
```bash
# See what changesets exist
npx changeset status
# Preview version bump
npx changeset version --dry-run
```
## Common Mistakes
| Mistake | Issue | Fix |
|---------|-------|-----|
| Wrong bump type | Unexpected version | Review decision guide above |
| Vague description | Poor CHANGELOG | Be specific about changes |
| Missing changeset | No release notes | Always add before PR |
| Multiple changesets | Fragmented notes | Combine related changes |
| Not including context | Hard to understand | Explain *why* not just *what* |
## Common Scenarios
For detailed examples of common scenarios including:
- Bug fixes, new features, breaking changes
- Multiple related changes
- Consolidated changesets
- Pre-release versions
- Best practices for descriptions
See **[Scenarios & Examples](references/scenarios.md)**
## References
- `{baseDir}/.changeset/config.json` - Changeset configuration
- `{baseDir}/CHANGELOG.md` - Generated changelog
- Changesets docs: https://github.com/changesets/changesets
## Related Skills
- **CI/CD automation**: See `managing-github-ci` for release workflow integration
- **Pre-commit validation**: See `validating-pre-commit` for quality gates before committingMore 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).

