Auto Research Agent
📖 技能介绍
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
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
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Based on karpathy/autoresearch by Andrej Karpathy.
🤖 AI 评测
这是一个偏重理论学习的参考 Skill，质量中等。优点是文档清晰易懂，能帮助你理解 AI 自主研究的基本原理和设计思路。不足之处是内容比较简单，只有概念介绍而缺乏实际案例演示，且本身不能执行任何代码。如果你只是想学习相关概念，这个 Skill 可以满足需求；如果你希望获得实际的操作体验，可能会觉得内容不够充实。
