Skill UI

This tool enables deep inspection and validation of web applications by connecting to the browser's DevTools Protocol (MCP). It allows agents to perform tasks like DOM inspection, capturing console logs, monitoring network requests, profiling performance, and taking screenshots in a live environment. Essential for debugging UIs, validating API integrations, and performing end-to-end automated testing.

This skill provides a comprehensive framework for measuring Go application performance. It guides users through writing robust benchmarks using `testing.B`, running tests with various flags (`-benchmem`, `-cpuprofile`), interpreting complex profiling data (pprof), statistically comparing results using tools like `benchstat`, and setting up CI regression detection. It is essential for deep performance analysis and making statistically sound optimization decisions.

This comprehensive guide provides best practices for developing robust and efficient data access layers using Entity Framework Core. It covers critical areas including DbContext design, performance optimization (e.g., AsNoTracking, Select projection), secure querying techniques, migration strategies, and testing methodologies, ensuring the creation of scalable and maintainable applications.

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

This guide enforces a mandatory, multi-stage process for debugging complex software issues. It moves developers beyond merely patching symptoms by forcing them through Root Cause Investigation, Pattern Analysis, Hypothesis Validation, and proper Implementation with testing. It is crucial for solving production bugs, failed tests, and performance issues in a structured manner.

This tool provides comprehensive load testing, auto-scaling simulation, and capacity planning for Canva Connect API integrations. It is essential for performance testing, managing complex rate limits (both per-user and global), and ensuring robust, scalable production workloads for Canva-dependent applications.

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

Comprehensive solution for conducting load testing, performance benchmarking, and auto-scaling setup for various Adobe APIs (Firefly, PDF Services, Photoshop). This guide utilizes k6 for stress testing, simulating real-world user traffic, and includes Kubernetes HPA configuration to ensure the application maintains high availability and stability under peak loads.

A comprehensive guide and set of scripts for performing load testing and capacity planning for Anthropic's Claude API. This resource helps developers benchmark performance, anticipate traffic spikes, manage rate limits (RPM/ITPM/OTPM), and implement robust horizontal scaling strategies for mission-critical AI services using Python.

This guide provides comprehensive methods for load testing and capacity planning specifically for Salesforce integrations. It walks users through calculating API limit budgets, running high-volume performance tests using tools like k6, and optimizing Bulk API throughput. Use this when scaling complex, high-volume, or mission-critical Salesforce processes.

This guide provides a comprehensive framework for load testing Shopify app integrations, specifically designed to respect strict API rate limits. It teaches users how to plan and benchmark system capacity for high-traffic events like Black Friday/Cyber Monday (BFCM) or flash sales using tools like k6. Users learn to optimize for burst events, analyze GraphQL cost, and configure infrastructure scaling for reliable e-commerce performance.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.