Testing Performance

Manage performance baselines and detect regressions from YAML-based benchmark data.

6|3|Updated Apr 12, 2026

Search Tags:#testing #performance #monitoring #benchmarking #regression #latency

Ready to run

npx skills add https://github.com/carson-sweet/sweetclaude --skill testing-performance-carson-sweet

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Testing Performance
Source: https://github.com/carson-sweet/sweetclaude/tree/main/skills/testing-performance
Command: npx skills add https://github.com/carson-sweet/sweetclaude --skill testing-performance-carson-sweet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml.

What problem does it solve?

This skill solves the lack of visibility into application performance by providing a structured way to track load scenarios, establish baselines, and identify regressions over time.

Core Features & Use Cases

Scenario Management: Define and track specific load scenarios for endpoints, background jobs, or CLI commands.
Regression Detection: Automatically compare current benchmark results against established baselines to flag performance degradation.
Use Case: Use this skill to monitor the p99 latency of your homepage load after every deployment to ensure it stays within defined thresholds.

Quick Start

Ask the assistant to define a new load scenario for your primary API endpoint to begin tracking performance metrics.

Frequently Asked Questions about Testing Performance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I track performance baselines and detect latency regressions?▼

Performance baselines are tracked by storing benchmark results in YAML state files. You define load scenarios with specific thresholds, and the system automatically compares current latency and error rate metrics against historical data to detect regressions.

What is automated performance regression detection for software applications?▼

Automated performance regression detection compares current benchmark results against established baselines. By defining load scenarios and thresholds, the system flags performance degradation across various operation types without requiring manual metric analysis.

How do I set up load scenarios to monitor p99 latency for API endpoints?▼

You can set up load scenarios by asking the assistant to define specific endpoints, background jobs, or CLI commands to track. The system then stores threshold settings and monitors p99 latency metrics against historical baselines using YAML state management.

Can I use YAML state management to store and compare performance metrics?▼

Yes, YAML state management is required to store and compare latency and error rate metrics against historical data. The system uses YAML files to maintain performance baselines and track benchmark results across various operation types over time.

Does this performance monitoring approach work for CLI commands and background jobs?▼

Yes, this performance monitoring supports tracking CLI commands and background jobs alongside API endpoints. You can define load scenarios for various operation types, establish baselines, and detect regressions across all supported command types.

What's the best way to monitor application performance after deployments?▼

The best way to monitor post-deployment performance is to establish baselines and define load scenarios with threshold settings. The system automatically compares current benchmark results against historical data to flag any performance degradation after each deployment.