Skill UI

Automated skill that assists with percentile analyzer workflows in performance testing, guiding users through load, stress, benchmarking, and monitoring setups with best practices.

Guides teams through automated performance baseline creation, offering templates, validation rules, and best-practice checklists for load, stress, and benchmarking workflows within performance testing efforts.

This skill provides comprehensive guidance on load testing and capacity planning for the Perplexity Sonar API. Users learn how to simulate high traffic volumes, respecting strict rate limits (like 50 RPM), benchmark latency, and estimate operational costs. It is essential for performance engineering, capacity architecture design, or stress testing APIs at scale.

This comprehensive guide provides detailed steps for load testing and capacity planning of Replit deployments. Learn how to benchmark performance using industry tools like k6 and autocannon, optimize Autoscale behavior, right-size Reserved VMs, and manage database connection pools. It covers optimizing cold start times and ensuring high availability under variable and peak traffic loads.

Automates security benchmark runner workflows by triggering on relevant terminology, guiding pen testing/compliance tasks, generating production-ready configs, and validating outputs against industry standards within advanced enterprise security scenarios.

Soak Test Planner automates guidance for long-duration performance testing; it triggers when soak test patterns are mentioned, helps configure load, stress, and benchmark scenarios, generates ready-to-run code and configs, and validates outputs against industry standards.

Automates spike test setup workflows by offering guided steps, best practices, code snippets, and validations across load/stress benchmarking and monitoring scenarios.

Automated guidance for thread dump analyst tasks within performance testing, offering best practices, configuration templates, code snippets, and validation support when working on load, stress, benchmark, or monitoring scenarios.

Automates throughput calculator tasks for Performance Testing by offering step-by-step guidance, industry best practices, and production-ready code for load, stress, benchmarking, and monitoring scenarios.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

Python framework for multi-objective optimization, offering NSGA-II/III, MOEA/D, benchmark suites, customizable operators, and visualization to help engineering teams explore Pareto fronts and make multi-criteria decisions.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.