auto-research

Automates iterative code improvement through systematic experimentation and metric optimization.

2|Updated Mar 10, 2026

Search Tags:#research #optimization #mlops #code improvement #experimentation #autonomous

Ready to run

npx skills add https://github.com/christyjacob4/claude-tricks --skill auto-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: auto-research
Source: https://github.com/christyjacob4/claude-tricks/tree/main/skills/auto-research
Command: npx skills add https://github.com/christyjacob4/claude-tricks --skill auto-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of iteratively improving code by running systematic experiments, keeping successful changes, and discarding failures, allowing for continuous optimization without constant human oversight.

Core Features & Use Cases

Autonomous Experimentation: Runs experiments on codebases to optimize a specific metric.
Iterative Improvement: Automatically keeps changes that improve the metric and reverts those that don't.
Resource Management: Can utilize local or remote GPUs for parallel execution.
Use Case: Imagine you have a machine learning model that needs its hyperparameters tuned. You can trigger this Skill to autonomously run hundreds of experiments overnight, exploring different learning rates, batch sizes, and architectures, and waking up to a significantly improved model.

Quick Start

Use the auto-research skill to autonomously experiment on the codebase to improve the val_bpb metric.

Frequently Asked Questions about auto-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate iterative code improvement and metric optimization for machine learning training?▼

To automate metric optimization, you need to define a specific target metric, identify modifiable code sections, provide a runnable experiment command, and ensure available compute resources like local or remote GPUs are accessible for parallel execution.

What is autonomous code experimentation and how does it work?▼

It requires a defined metric, modifiable code sections, a runnable experiment command, and available compute resources to systematically test changes, retain improvements, and discard failures for continuous optimization.

Can I use autonomous experimentation for compiler optimization and algorithm tuning?▼

It supports any scenario where you have a defined metric, modifiable code sections, a runnable experiment command, and available compute resources, making it suitable for compiler optimization and algorithm tuning.

Do I need remote GPUs to run autonomous code experimentation loops?▼

The Skill supports both local and remote GPUs, allowing you to leverage available compute resources to run parallel experiments for optimizing your defined metric efficiently.

How do I set up autonomous hyperparameter tuning overnight?▼

The Skill automates this process by exploring different parameters like learning rates and batch sizes, keeping successful changes that improve the metric, and reverting failures, allowing you to wake up to an improved model.

What are the limitations of automating code experimentation and metric optimization?▼

It is limited by the quality of the defined metric and the availability of compute resources, as the process relies on systematically running experiments and evaluating metric improvements to retain or discard code changes.