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Use when managing Azure Artifacts — feed creation and permissions, upstream sources, retention policies, package promotion across views, and connecting build pipelines to artifact feeds.
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| name | ado-artifacts |
| description | Use when managing Azure Artifacts — feed creation and permissions, upstream sources, retention policies, package promotion across views, and connecting build pipelines to artifact feeds. |
# Publish a Universal Package to a feed
az artifacts universal publish \
--organization https://dev.azure.com/MyOrg \
--project MyProject \
--scope project \
--feed MyFeed \
--name my-package \
--version 1.0.0 \
--path ./dist
Feed scope:
Views: @local (all versions) → @prerelease (tested) → @release (production-ready). Promote packages between views as quality gates.
Configure via UI: Feed → Settings → Upstream sources → Add upstream
Common upstreams:
| Package type | Upstream | Use |
|---|---|---|
| npm | npmjs.com | Public npm packages |
| NuGet | NuGet Gallery | Public .NET packages |
| PyPI | pypi.org | Public Python packages |
| Maven | Maven Central | Public Java packages |
Key benefit: Developers use ONE feed URL, packages from upstream auto-cached. Enables audit and governance of public package versions.
Feed permissions levels:
Best practice:
Build service account → Contributor (publish from CI)
Developers → Collaborator or Reader
Admins only → Owner
Configure: Feed → Settings → Retention policies
@release view (protected)# azure-pipelines.yml
- task: NuGetAuthenticate@1
- task: NuGetCommand@2
inputs:
command: restore
feedsToUse: select
vstsFeed: 'MyProject/MyFeed'
ado-pipelines-ops — pipeline service connections used to authenticate to feedsado-security-policies — feed permissions align with project security modelado-api-cli — automate package publishing and promotionUse when conducting quality audits — reviewing process compliance, identifying gaps between defined process and actual practice, conducting structured inspections (code review audits, test quality reviews), and producing audit reports with remediation plans.
Use when managing quality risk — identifying quality risks in a product or release, applying risk-based testing prioritization, creating risk mitigation plans, and communicating quality risk to stakeholders for go/no-go decisions.
Use when designing or implementing test automation — choosing the right automation framework (Playwright, pytest, JUnit), Page Object Model, selector strategies, test isolation, managing flaky tests, and CI integration.
Use when managing defects — writing effective bug reports, applying severity/priority matrix, tracking defect lifecycle, conducting root cause analysis, and measuring defect metrics for process improvement.
Use when measuring and reporting QA quality — defect escape rate, test coverage analysis, flaky test rate, mean time to detect, shift-left metrics, and building quality dashboards for stakeholders.
Use when managing test data — designing test data strategies, using factories and builders, creating fixtures, generating synthetic data, masking PII for testing, and managing test database state.