deployment-strategy
Design and manage release pipelines — from commit to production. Use when setting up CI/CD, planning a release, rolling back, or choosing between deployment strategies (blue-green, canary, feature flags, progressive delivery).
Works with
--- name: deployment-strategy description: Design and manage release pipelines — from commit to production. Use when setting up CI/CD, planning a release, rolling back, or choosing between deployment strategies (blue-green, canary, feature flags, progressive delivery). license: MIT --- # Deployment Strategy Ship reliably at any scale. ## The Ideal Pipeline ``` commit → lint+typecheck → unit tests → build → integration tests → staging → E2E → production ``` Every stage must be fast enough that developers do not bypass it. If CI takes 30 minutes, developers will stop waiting for it. ## Deployment Strategies | Strategy | Risk | Speed | Complexity | Best for | |----------|------|-------|------------|----------| | **Feature flags** | Lowest | Instant on/off | Medium | Anything | | **Canary** | Low | Gradual | High | Traffic-sensitive changes | | **Blue-green** | Low | Immediate switch | Medium | Infra changes | | **Rolling update** | Medium | Per-instance | Low | Stateless services | | **Recreate** | Highest | Full swap | None | Dev environments | **Prefer feature flags** for application-level changes. They decouple deployment from release — you can deploy unfinished code safely and release features when ready. ## Rollback Strategy Every deployment must have a rollback plan tested before the deploy: 1. Can we revert the code? (git revert is not enough — DB migrations may not be reversible) 2. Can we toggle the feature off? (feature flags are the fastest rollback) 3. How long does rollback take? (if > 5 minutes, you need a faster mechanism) ## Progressive Delivery Ship to a subset of users first: 1. **Internal** — deploy to dev/staging, validate with team 2. **Beta** — deploy to 5% of users, monitor errors and performance 3. **Gradual rollout** — 25% → 50% → 75% → 100%, with monitoring gates at each step 4. **Full release** — 100% with kill switch ready ## Database Migrations The riskiest part of any deploy. Rules: - Migrations must be backward-compatible (old code works with new schema) - Split into phases: schema change → deploy code → backfill data → remove old columns - Never do a migration that locks a production table for more than a few seconds - Use a tool (Sqitch, Prisma Migrate, Flyway) — raw SQL scripts in a README do not count ## References See `references/deployment-playbook.md` for runbooks for common deploy scenarios. See `references/rollback-checklist.md` for the pre-deploy rollback verification checklist.
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).

