uv-hands-on-learning

What problem does it solve?

This Skill provides a structured, evidence-based approach to validating performance claims and identifying bottlenecks in ML/LLM repositories, turning abstract analysis into concrete, reproducible experiments.

Core Features & Use Cases

• Reproducible Experimentation: Set up, execute, and report on ML/LLM experiments with a focus on reproducibility.
• Environment Capture: Automatically document the exact hardware, software, and tooling used for experiments.
• Performance Analysis: Profile and benchmark code to understand performance characteristics and identify areas for optimization.
• Use Case: You've read a paper claiming a new LLM architecture achieves state-of-the-art throughput. Use this Skill to set up a session, clone the repo, run the benchmarks under controlled conditions, capture the environment, and report on whether the claims hold true, providing evidence for your findings.

Quick Start

Run the uv-hands-on-learning skill to start a new hands-on learning session for the vllm project repository.