Does this skill support Kubernetes scaling?

Yes, it includes patterns for horizontal scaling using deployments and auto-scaling based on task queue depth metrics.

Can it help with worker OOM (Out of Memory) issues?

Absolutely. It provides memory estimation guides and helps you limit concurrent executions to prevent resource exhaustion.

What parameters does this skill help configure?

It provides guidance on MaxConcurrentActivityExecutionSize, MaxConcurrentWorkflowTaskExecutionSize, WorkerActivitiesPerSecond, and poller settings.

How do I calculate the ideal concurrency for my workers?

The skill provides a formula based on available CPU cores, memory per activity, and external service rate limits to find the optimal balance.

What is the benefit of using dedicated task queues?

Dedicated queues allow you to isolate different workloads (e.g., CPU-intensive vs. I/O-bound) so they can be scaled and tuned independently.

Temporal Worker Tuning

Name: Temporal Worker Tuning
Author: therealbill

bytherealbill

•

Deployment & DevOps

Optimizes Temporal worker performance through precise configuration of concurrency limits, task queue strategies, and resource management.

The worker-tuning skill provides expert guidance for configuring and scaling Temporal workers to achieve maximum efficiency and reliability. It helps developers navigate complex parameters like MaxConcurrentActivityExecutionSize and poller counts, providing tailored advice based on whether tasks are CPU-bound, I/O-bound, or resource-constrained. By implementing the patterns suggested by this skill, users can reduce schedule-to-start latency, manage memory consumption effectively, and implement sophisticated scaling strategies like dedicated task queues and sticky execution.

Key Features

013 GitHub stars

02Expert concurrency limit calculations for CPU and I/O tasks

03Resource-aware memory and CPU management guidelines

04Poller configuration optimization for high-throughput queues

05Horizontal and vertical scaling patterns for Kubernetes

06Real-time performance monitoring and troubleshooting metrics

Use Cases

01Reducing task queue latency in high-volume production environments

02Optimizing worker footprints for resource-constrained cloud environments

03Implementing priority-based task processing and worker partitioning

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add therealbill/mynet worker-tuning

For use in Claude.ai and ChatGPT