| Prepare and publish a new version of the waza azd extension. "prepare release", "update changelog", "azd publish", "new release", "version bump", "cut a release". CI/CD pipeline changes (edit workflow files directly).
npx skills add https://github.com/microsoft/waza --skill azd-publish
> Automate version bumps, changelog updates, and PR creation for waza azd extension releases.
Follow these steps in order. Ask the user for input at each decision point.
Get the current version from version.txt and extension.yaml, then collect commits since the last release:
cat version.txt
# Find the latest azd extension version tags
git tag --list 'azd-ext-microsoft-azd-waza_*' --sort=-v:refname | head -5
# Get commits since last azd extension tag
last_tag=$(git tag --list 'azd-ext-microsoft-azd-waza_*' --sort=-v:refname | head -1)
git log "${last_tag}..HEAD" --oneline --no-decorate
If version.txt and extension.yaml differ, flag it to the user before proceeding.
Summarize the changes grouped by type:
feat: commitsfix: commitsrefactor:, chore:, docs: commitsPresent the summary to the user for review.
Then update CHANGELOG.md. The changelog follows Keep a Changelog format.
Perform these updates (using a placeholder version X.Y.Z — the actual version is determined in Step 2):
## [Unreleased] into a staging area. If [Unreleased] is empty, populate from the git log summary gathered above.### Added, ### Fixed, ### Changed as appropriate based on the commits gathered above.Hold these changelog entries — the new version section header and comparison links will be finalized after the version is determined in Step 2.
Based on the changes gathered in Step 1, recommend a version bump type using standard semver semantics:
feat!:, BREAKING CHANGE:) → (MAJOR+1).0.0feat:) → MAJOR.(MINOR+1).0fix:, docs:, refactor:, chore:) → MAJOR.MINOR.(PATCH+1)Present the recommendation with rationale (e.g., "I see 3 feat: commits and no breaking changes — recommending a minor bump").
ASK THE USER to confirm the recommended bump or choose a different one.
Compute the new version and confirm with the user before proceeding.
Then finalize the changelog:
## [Unreleased] with today's date: ## [X.Y.Z] - YYYY-MM-DD
[Unreleased]: https://github.com/microsoft/waza/compare/azd-ext-microsoft-azd-waza_X.Y.Z...HEAD
[X.Y.Z]: https://github.com/microsoft/waza/compare/azd-ext-microsoft-azd-waza_PREVIOUS...azd-ext-microsoft-azd-waza_X.Y.Z
## [Unreleased] with empty subsections or blank.Update these files with the new version:
version.txt — Replace contents with new version stringextension.yaml — Update the version: fieldShow the user a summary of all changes made:
version.txt, extension.yaml, CHANGELOG.mdgit diffASK THE USER: Should I create a PR with these changes?
If yes:
git checkout -b release/v{VERSION}
git add version.txt extension.yaml CHANGELOG.md
git commit -m "chore: Prepare release v{VERSION}"
git push origin release/v{VERSION}
gh pr create \
--title "Release v{VERSION}" \
--body "## Release v{VERSION}
### Changes
{changelog entries for this version}
### Checklist
- [ ] Version bumped in version.txt and extension.yaml
- [ ] CHANGELOG.md updated
- [ ] CI passes
- [ ] Ready to publish via 'Publish azd Extension' workflow" \
--base main \
--head release/v{VERSION}
If no:
| File | Purpose | What Gets Updated |
|------|---------|-------------------|
| version.txt | Single source of version truth | New semver version string |
| extension.yaml | azd extension manifest | version: field |
| CHANGELOG.md | Human-readable change history | New version section with entries |
feat:, fix:, chore:, docs:, refactor:) when interpreting git historyrelease/v{VERSION}azd-ext-release.yml) to build, pack, and publish the extensionThis skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Take microsoft/azd-publish from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.