By name: cloud-native-engineer description: The definitive skill for building and deploying high-performance, distributed systems using Cloud Native standards (Dapr, Redis, Microservices). Use when a project requires professional-grade architecture, cross-service communication, elastic scaling, and sub-second agentic latency. Mandatory for flawless deployments on Kubernetes (Local or Cloud).
Cloud Native Engineer (Master Skill)
This skill transforms Claude into an elite Cloud Native Architect capable of delivering production-ready distributed systems.
Core Capabilities
- Strategic Domain Decomposition: Logic for splitting any monolith into clear microservice boundaries.
- Standardized Dapr Infrastructure: Reliable, ready-to-use configurations for Pub/Sub, State, and Jobs.
- Flawless K8s Orchestration: Deterministic deployment workflows that avoid DNS, Auth, and Probe failures.
- Agentic Performance (Sub-Second): Engineering patterns for ultra-fast AI interactions.
Workflows and Procedures
1. Architectural Planning
Analyze service boundaries and define the shared communication backbone.
- Guidance: MICROSERVICES.md (Domain splitting & Payload standards).
2. Infrastructure & Containerization
Build elite, secure, and lean images before rolling out the backbone.
- Docker Best Practices: DOCKER_BEST_PRACTICES.md.
- Backbone Setup: DAPR_CONFIG.md (Redis-first Pub/Sub and State yaml).
3. Fail-Proof Deployment (Mandatory Sequence)
Follow the EXACT order of operations to ensure 100% success.
- Deployment Guide: DEPLOYMENT_GUIDE.md.
- Manifest Standards: MANIFEST_STANDARDS.md.
4. Automated Orchestration
Use the "Low Freedom" scripts to automate repetitive tasks.
- Deploy Minikube: scripts/deploy_minikube.ps1.
- Verify Cluster: scripts/verify_cluster.ps1.
5. Diagnostic Excellence
Diagnose "Silent Failures" using the troubleshooting matrix.
- Matrix: TROUBLESHOOTING.md.
6. AI Acceleration (SaaS Specialty)
Implement the persistent MCP pattern for sub-second chatbot responses.
- Reference: LATENCY_OPTIMIZATION.md.
Elite Checklist for Agents
- Network-Parity: Internal calls use K8s service names (
http://service:port). - Probe-Resilience: Liveness probes have enough delay for sidecar startup.
- Cluster-Auth:
JWKS_URLpoints to the internal ingress/service. - Warm-Start: AI Tools are pre-initialized in the application lifespan.
AI Toolbox & Standards
To ensure maximum "intelligence" and deterministic outcomes, use the following tools and patterns:
1. Diagnostic Tools
| Tool | Description | Parameters | When to Use |
|---|---|---|---|
kubectl logs |
Retrieve backend/frontend logs. | -n <namespace> -c <container> --tail=<N> |
For 500 errors or startup failures. |
kubectl exec |
Run commands inside a pod. | -n <namespace> -c <container> -- <command> |
For database checks, file verification. |
kubectl describe |
Detailed status of a resource. | pod <name> -n <namespace> |
For CrashLoopBackOff or pending pods. |
2. Implementation Patterns
- Cascade Deletes: Always use
sa_column_kwargs={"ondelete": "CASCADE"}for foreign keys referencing parent entities (e.g.,task_idinTaskTag). - Path Resilience: Use absolute paths (
/app/...) inside containers to avoid "No such file" errors. - Dapr-First: Use
event_publisherfor cross-service events to maintain decoupled architecture. - SQLModel Standards: Use
Session(engine)context managers for reliable database transactions and commits.
3. Verification Protocol
- Logs: Check backend logs for tracebacks.
- Reproduction: Create a minimal python script inside the pod to isolate DB/Logic errors.
- Fix & Verify: Apply the fix, re-run the reproduction script, and verify via frontend/CLI.