Skill UI

This guide provides comprehensive strategies for scaling Customer.io integrations to handle high volumes of daily events. It covers implementing k6 load tests for performance benchmarking and outlines architectural patterns—from simple Direct APIs to complex Redis/Kafka queue-backed systems—ensuring reliability and managing API rate limits for massive data throughput.

Offers automated guidance for database query profiler tasks in performance testing, including best practices, code/config generation, and validation when working with load/stress benchmarking.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

Automates flame graph generation during performance testing, guiding users through best practices, sample code, and validation for load, stress, and benchmarking workflows.

Automates Gatling scenario creation for performance testing, offering guidance, production-ready code, validations and best-practice patterns for load, stress, and benchmarking workflows.

Automates GC log analyzer workflows for performance testing by guiding load, stress, benchmarking, and monitoring efforts with best-practice advice and ready-to-use configurations.

This skill simulates a senior Go developer, specializing in writing high-performance, production-grade Go applications. It covers advanced topics like concurrent programming using goroutines and channels, designing robust microservices with gRPC/REST, and optimizing code using pprof and benchmarks. It enforces idiomatic Go patterns, context propagation, and strict testing practices (table-driven, fuzzing, race detection).

A tool designed to collect comprehensive diagnostic evidence for troubleshooting issues encountered with the Groq API. It captures environment details, API connectivity status, rate limit information, latency benchmarks, and redacted application logs, making it essential for creating detailed and actionable support tickets.

Automates JMeter test plan creation and validation for load, stress, and benchmarking scenarios; triggers whenever you request the jmeter test plan creator and delivers production-ready configs aligned with best practices.

Auto-activating helper that provides step-by-step guidance, best practices, and validation for generating production-ready k6 load test scripts, configs, and benchmarking workflows.

This comprehensive guide provides strategies for optimizing video generation using Kling AI. It covers the crucial trade-offs between speed, quality, and cost by selecting the optimal model and parameters. Techniques detailed include performance benchmarking, implementing connection pooling, advanced caching strategies, and prompt engineering best practices to ensure efficient and high-quality outputs for any production use case.