Auto Research Agent

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name: autoresearch-agent description: "A reference framework for understanding autonomous AI research pipelines. Learn how AI can optimize ML training with fixed time budgets and metric-driven iteration." metadata: openclaw: requires: bins: - python3 os: - linux

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AutoResearch Framework

A reference guide for understanding how autonomous AI research works. This skill documents the methodology from karpathy/autoresearch for educational purposes.

What This Is

This skill does NOT run any code. It serves as a reference for understanding:

Fixed time budget experiments (5 minutes)

Metric-driven iteration (val_bpb)

Single-file training scope

Self-contained ML training setup

Key Concepts

| Concept | Description | |---------|------------| | val_bpb | Validation bits per byte — lower is better | | Fixed Budget | Experiments run for exactly 5 minutes | | Single Scope | One file to modify per experiment |

Architecture Overview

The framework consists of three files:

| File | Purpose | |------|---------| | prepare.py | Data preparation (do not modify) | | train.py | Model training loop reference | | program.md | Research strategy template |

Design Patterns

Fixed time budget: Makes experiments directly comparable

Single file scope: Keeps changes manageable

Metric-driven: Uses val_bpb to compare results

For Educational Use

This skill is a reference implementation based on karpathy/autoresearch by Andrej Karpathy. It demonstrates autonomous research methodologies used in modern AI development.

Inspiration

Based on karpathy/autoresearch by Andrej Karpathy.