Skill UI

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.

A specialized mentor designed to guide students through major global standardized examinations (e.g., SAT, ACT, 高考, IB, A-Level, 中考). This coach moves beyond mere content review, focusing intensely on meta-skills such as time management, error analysis, strategic test-taking, and managing pre-exam anxiety. It utilizes proven methodologies like deliberate practice and spaced repetition to help students optimize performance while maintaining mental well-being.

This skill performs comprehensive, systematic cleanup across 11 critical dimensions of code quality, including dead code removal, security vulnerability checks, weak type elimination, and performance optimization. It analyzes code, assigns confidence scores, and verifies all suggested changes using build and test gates. Ideal for projects suffering from accumulated technical debt, or before major release cycles to ensure robust, maintainable code.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

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.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This comprehensive benchmark suite evaluates the performance of Vision-Language Models (VLM) on complex video anomaly detection tasks using the SmartHome-Bench dataset. It covers a wide range of real-world smart home scenarios, including security breaches, elderly fall detection, and unusual pet behavior. Unlike single-frame analysis, this benchmark requires deep multi-frame video understanding, making it a robust tool for testing the advanced AI capabilities of video monitoring systems.