Can this skill help with database sizing?

Yes, it provides specific CPU, memory, storage, and IOPS recommendations for PostgreSQL based on total workflow volume and retention requirements.

Why are history shards so important in Temporal?

History shards determine the maximum workflow parallelism. Because they cannot be changed after a cluster is created, getting the initial count right is critical for long-term scalability.

How do I know when to scale my cluster services?

The skill identifies key metrics like sustained CPU utilization over 70%, memory over 80%, or p99 request latency as triggers for horizontal or vertical scaling.

Does it provide deployment configurations?

Yes, it includes YAML templates for small (dev/test), medium (production start), and large (high volume) cluster profiles including resource requests and limits.

Temporal Cluster Sizing

Name: Temporal Cluster Sizing
Author: therealbill

bytherealbill

0•

云基础设施

Provides expert guidance and capacity planning for Temporal clusters, focusing on shard allocation and resource optimization.

The Temporal Cluster Sizing skill empowers developers and SREs to accurately architect and scale Temporal orchestration engines by providing data-driven infrastructure recommendations. It offers precise calculations for history shards—a critical, immutable configuration—as well as replica counts and resource requirements for Frontend, History, and Matching services. By leveraging this skill within Claude Code, users can avoid common pitfalls like undersized persistence layers or insufficient shard counts, ensuring their workflow engine handles production loads ranging from 10,000 to over 2 million concurrent workflows with high availability.

主要功能

01Provide database and storage sizing for PostgreSQL and Elasticsearch persistence layers

020 GitHub stars

03Calculate immutable history shard requirements based on concurrent workflow projections

04Access pre-configured YAML templates for small, medium, and large cluster deployment profiles

05Generate replica and resource recommendations for core Frontend, History, and Matching services

06Monitor key performance metrics to drive horizontal and vertical scaling decisions

使用场景

01Initial architecture and capacity planning for new production Temporal clusters

02Troubleshooting performance bottlenecks and latency spikes in History or Matching services

03Scaling existing clusters to handle increased workflow throughput and parallelism

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add therealbill/mynet cluster-sizing

For use in Claude.ai and ChatGPT

Download Skill