What tools are required to use this skill effectively?

To get the most out of this skill, you should have the k6 load testing tool installed and access to a Kubernetes cluster for deployment scaling.

How does this skill help with Retell AI performance?

It provides automated scripts for k6 to simulate high traffic and identifies latency bottlenecks or rate limits before they impact your end users.

How does it calculate capacity headroom?

The skill uses a TypeScript utility to analyze current RPS and CPU utilization, estimating the maximum requests your system can handle while maintaining a 70% safety target.

Can I use this for Kubernetes deployments?

Yes, it includes pre-configured HPA templates to scale your Retell AI integration pods automatically based on CPU usage or custom queue depth metrics.

Retell AI Performance & Scaling

Name: Retell AI Performance & Scaling
Author: micsapp

bymicsapp

0•

Seguridad y Pruebas

Optimizes Retell AI integrations through automated load testing, Kubernetes auto-scaling configurations, and data-driven capacity planning.

This skill provides a comprehensive toolkit for managing the performance and scalability of applications utilizing Retell AI voice agents. It enables developers to implement sophisticated k6 load testing scripts with defined latency thresholds, configure Kubernetes Horizontal Pod Autoscalers (HPA) for dynamic traffic handling, and establish robust connection pooling strategies. By leveraging pre-built metrics benchmarks and capacity estimation logic, it ensures that voice AI integrations remain stable, cost-effective, and responsive under varying demand levels, preventing common issues like rate limiting or resource exhaustion.

Características Principales

01Kubernetes Horizontal Pod Autoscaler (HPA) configurations for voice agents

02Standardized performance benchmarking templates for production audits

03Automated k6 load testing scripts with custom performance thresholds

04Capacity planning templates and real-time headroom estimation

05TypeScript-based connection pooling and client resource management

060 GitHub stars

Casos de Uso

01Estimating infrastructure requirements and API costs for projected enterprise user growth

02Implementing auto-scaling for an AI call center application to handle unpredictable traffic spikes

03Stress testing Retell AI voice agents before a major marketing campaign or product launch

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add micsapp/micstec-skills retellai-load-scale

For use in Claude.ai and ChatGPT

Características Principales

01Kubernetes Horizontal Pod Autoscaler (HPA) configurations for voice agents

02Standardized performance benchmarking templates for production audits

03Automated k6 load testing scripts with custom performance thresholds

04Capacity planning templates and real-time headroom estimation

05TypeScript-based connection pooling and client resource management

060 GitHub stars

Casos de Uso

01Estimating infrastructure requirements and API costs for projected enterprise user growth

02Implementing auto-scaling for an AI call center application to handle unpredictable traffic spikes

03Stress testing Retell AI voice agents before a major marketing campaign or product launch