release-engineering
Plan and verify software releases with versioning, changelogs, release branches, feature flags, canaries, migration gates, rollback, deployment checks, and release readiness. Use when preparing a release, shipping a risky PR, coordinating app/backend/database rollout, recovering from a bad deploy, or defining release policy for a repo.
Works with
--- name: release-engineering description: Plan and verify software releases with versioning, changelogs, release branches, feature flags, canaries, migration gates, rollback, deployment checks, and release readiness. Use when preparing a release, shipping a risky PR, coordinating app/backend/database rollout, recovering from a bad deploy, or defining release policy for a repo. license: MIT --- # Release Engineering ## Purpose Use this skill to turn "ready to merge" into a controlled release. It connects code, data, config, deployment, monitoring, and rollback. ## Release Classification Classify the release before choosing gates: | Type | Extra Gates | |---|---| | Patch fix | focused regression test, rollback proof | | Feature release | feature flag, docs/changelog, product acceptance | | API or SDK change | versioning, deprecation, compatibility tests | | Database migration | `data-contract-migrations`, backup, backfill, recovery | | Config or secret change | `config-secrets-environments`, rotation, drift check | | Infrastructure change | canary, health checks, capacity and rollback | ## Release Plan For non-trivial releases, require: 1. Version or release identifier. 2. Changelog entries grouped by user impact. 3. Build artifacts and provenance. 4. Migration and config gates. 5. Canary or staged rollout plan. 6. Monitoring dashboard and alert expectations. 7. Rollback or roll-forward command. 8. Owner, decision time, and abort criteria. ## Verification Use fresh commands from the current session. Typical gates: ```bash git status --short git log --oneline -n 5 # project-specific build/test command # migration dry run or validation query # deployment health check ``` Do not claim a release shipped unless remote git, CI, artifact, and runtime state have each been checked at the required level. ## Output Shape ```text release_scope: version_or_identifier: included_changes: excluded_changes: pre_release_checks: rollout_steps: migration_and_config_gates: monitoring: rollback_or_recovery: go_no_go_status: ```
More 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).

