Conducts autonomous experimentation loops to iteratively optimize code performance based on measurable metrics.
The Auto-Research skill transforms Claude into an autonomous scientist capable of running systematic experiment loops on your codebase. It identifies key performance metrics—such as accuracy, latency, or model loss—and generates hypotheses to improve them. By utilizing Git worktrees for isolation, it can run experiments sequentially or in parallel across multiple local or remote GPUs. It automatically keeps successful improvements, discards failures, and maintains a detailed results log, making it ideal for 'overnight' optimization tasks like hyperparameter tuning, architecture search, and algorithmic benchmarking.
Key Features
01Support for multi-GPU and remote SSH compute environments
02Resilient crash handling with automatic recovery and reversion
031 GitHub stars
04Automatic metric tracking and logging via results.tsv
05Git worktree isolation for safe, parallel experimentation
06Autonomous hypothesis generation and iterative testing loop
Use Cases
01Optimizing machine learning model hyperparameters overnight
02Conducting automated ablation studies to simplify codebases
03Benchmarking and reducing latency in high-performance algorithms
