temporal-workertuning

name	temporal-workertuning
description	Tune Temporal Worker performance across Go, Java, TypeScript, Python, and .NET. Use when the user asks to "configure slot suppliers", "set up a worker tuner", "enable poller autoscaling", "tune worker cache", "fix high schedule-to-start latency", "diagnose worker bottlenecks", "right-size workers for production", "choose between fixed-size and resource-based slot suppliers", "configure maxConcurrentWorkflowTaskExecutionSize", "set up resource-based tuner", "interpret worker_task_slots_available", "reduce schedule_to_start_latency", "scale worker fleet", "configure sticky cache", or mentions Worker tuning, Worker performance, slot suppliers, composite tuners, or poller autoscaling.
version	0.1.0

Skill: temporal-workertuning

Overview

Temporal Workers poll Task Queues, execute Workflow and Activity Tasks, and report results back to the Temporal Service. Worker performance depends on three resource dimensions: compute (concurrent task execution slots), memory (workflow cache), and IO (pollers and network). This skill provides guidance for tuning all three dimensions across Go, Java, TypeScript, Python, and .NET SDKs.

The key abstractions are Slot Suppliers (which control how many tasks a Worker accepts) and Worker Tuners (which assign slot suppliers to different task types). Workers can use fixed-size suppliers for predictable workloads, resource-based suppliers for automatic adjustment, or custom suppliers for full control.

When to Use This Skill

User intent	Start here
Understand tuning concepts (slots, suppliers, tuners)