Agent skill
canary-deployment
Implement canary deployment strategies to gradually roll out new versions to subset of users with automatic rollback based on metrics.
Install this agent skill to your Project
npx add-skill https://github.com/aj-geddes/useful-ai-prompts/tree/main/skills/canary-deployment
SKILL.md
Canary Deployment
Table of Contents
- Overview
- When to Use
- Quick Start
- Reference Guides
- Best Practices
Overview
Deploy new versions gradually to a small percentage of users, monitor metrics for issues, and automatically rollback or proceed based on predefined thresholds.
When to Use
- Low-risk gradual rollouts
- Real-world testing with live traffic
- Automatic rollback on errors
- User impact minimization
- A/B testing integration
- Metrics-driven deployments
- High-traffic services
Quick Start
Minimal working example:
# canary-deployment-istio.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-v1
namespace: production
spec:
replicas: 3
selector:
matchLabels:
app: myapp
version: v1
template:
metadata:
labels:
app: myapp
version: v1
spec:
containers:
- name: myapp
image: myrepo/myapp:1.0.0
ports:
- containerPort: 8080
---
// ... (see reference guides for full implementation)
Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Istio-based Canary Deployment | Istio-based Canary Deployment |
| Kubernetes Native Canary Script | Kubernetes Native Canary Script |
| Metrics-Based Canary Analysis | Metrics-Based Canary Analysis |
| Automated Canary Promotion | Automated Canary Promotion |
Best Practices
✅ DO
- Follow established patterns and conventions
- Write clean, maintainable code
- Add appropriate documentation
- Test thoroughly before deploying
❌ DON'T
- Skip testing or validation
- Ignore error handling
- Hard-code configuration values
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
websocket-implementation
Implement real-time bidirectional communication with WebSockets including connection management, message routing, and scaling. Use when building real-time features, chat systems, live notifications, or collaborative applications.
refactor-legacy-code
Modernize and improve legacy codebases while maintaining functionality. Use when you need to refactor old code, reduce technical debt, modernize deprecated patterns, or improve code maintainability without breaking existing behavior.
Sentiment Analysis
Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis
flask-api-development
Develop lightweight Flask APIs with routing, blueprints, database integration, authentication, and request/response handling. Use when building RESTful APIs, microservices, or lightweight web services with Flask.
ML Model Explanation
Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
Statistical Hypothesis Testing
Conduct statistical tests including t-tests, chi-square, ANOVA, and p-value analysis for statistical significance, hypothesis validation, and A/B testing
Didn't find tool you were looking for?