canary
Canary deployment patterns
Works with
---
name: canary
description: Canary deployment patterns
license: MIT
---
## What I do
- Implement gradual traffic shifting strategies
- Create automated canary deployments with metric analysis
- Design rollback automation based on health checks
- Build observability dashboards for canary analysis
- Integrate canary releases with CI/CD pipelines
- Configure intelligent traffic routing based on user segments
## When to use me
- When introducing new features to production
- When validating infrastructure changes
- When testing performance under real load
- When deploying to multiple regions progressively
- When working with A/B testing alongside deployments
- When reducing blast radius of potential issues
## Key Concepts
### Flagger Canary CRD
```yaml
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: myapp
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp
service:
port: 80
canaryAnalysis:
interval: 1m
threshold: 10
maxWeight: 50
stepWeight: 10
metrics:
- name: request-success-rate
threshold: 99
interval: 1m
- name: request-duration
threshold: 500
interval: 1m
```
### Deployment Strategy
- Start with small percentage (1-5%)
- Increase gradually if metrics stay healthy
- Stop and rollback if errors spike
- Analyze at each stage before proceeding
- Full rollout typically takes 30-60 minutes
### Analysis Criteria
- Error rate within acceptable threshold
- Response time not degraded
- No memory leaks or resource exhaustion
- Business metrics not negatively impacted
- No adverse effects on dependent servicesMore 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).

