Skill UI

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

Provides comprehensive strategies to optimize Langfuse tracing overhead for high-throughput applications. This guide covers benchmarking, tuning batch processing configurations, implementing non-blocking wrappers, and optimizing payload sizes. Use this when scaling Langfuse for production workloads to minimize latency and maximize stability.

Automates load test scenario planning by guiding through best practices, generating production-ready configurations, and validating load, stress, benchmarking and performance monitoring activities.

Locust Test Creator automates performance-testing guidance, best-practice verification, and ready-to-run code/config generation whenever you request locust-specific load, stress, benchmark, or monitoring scenarios.

This comprehensive skill manages the entire lifecycle of database testing, ensuring data integrity and system reliability. It covers setting up isolated test environments (using Docker or in-memory databases), executing migrations, and implementing robust test isolation strategies (such as transaction rollback). Use cases include verifying CRUD operations, validating referential constraints, performance benchmarking (EXPLAIN ANALYZE), and ensuring backward compatibility across multiple databases (PostgreSQL, MySQL, MongoDB) and ORMs.

Enterprise NeMo Evaluator SDK runs over 100 benchmarks from 18+ harnesses with multi-backend execution (Docker, Slurm, cloud) to give reproducible, scalable LLM evaluation workflows.

Guides performance testing teams through network latency tester tasks by suggesting best practices, generating production-ready scripts/configs, and validating results across load, stress, benchmarking, and monitoring scenarios.

This skill provides comprehensive best practices and Python code examples for optimizing API calls to Large Language Models (LLMs) via OpenRouter. It covers benchmarking model latency, implementing streaming to achieve low Time-To-First-Token (TTFT), and processing requests concurrently using asynchronous programming (asyncio). Use this when developing real-time, high-throughput applications that require predictable and fast model responses.

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.

Coordinate Productlane operations through Rube MCP and Composio tooling, covering discovery, connection checks, execution, bulk workbench runs, and schema adherence so dynamic integrations stay reliable.