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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
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npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill mle-agent-guideコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SOC 職業分類に基づく
SKILL.md を表示中
| name | mle-agent-guide |
| description | Intelligent companion for ML engineering with arXiv integration |
| version | 1.0.0 |
| author | wentor-community |
| source | https://github.com/MLSys-Tools/MLE-agent |
| metadata | {"openclaw":{"category":"research","subcategory":"automation","emoji":"🔬","keywords":["machine-learning","ml-engineering","arxiv-integration","experiment-tracking","model-development","ai-engineering"]}} |
A skill for using an intelligent ML engineering companion that integrates arXiv paper discovery with experiment implementation, tracking, and iteration. Based on MLE-agent (2K stars), this skill helps researchers bridge the gap between reading about new ML techniques and implementing them in their own projects.
Machine learning research moves at an extraordinary pace, with hundreds of new papers appearing on arXiv daily. Researchers struggle not just to keep up with the literature but to translate promising ideas into working implementations. MLE-agent addresses this by combining paper discovery, technique extraction, implementation assistance, and experiment management into a unified workflow.
This skill is designed for ML researchers and engineers who want to quickly prototype ideas from papers, systematically compare approaches, and maintain organized experiment records throughout the research process.
The skill provides sophisticated arXiv paper discovery and analysis:
Paper Discovery
Paper Analysis
Technique Extraction
The core experiment management workflow:
Project Setup
Implementation Assistance
Experiment Execution
Result Analysis
The skill enforces ML engineering standards throughout the workflow:
Reproducibility
Code Quality
Resource Management
The skill recognizes and supports common research patterns:
Baseline Comparison - Implement and evaluate standard baselines before proposing improvements Ablation Study - Systematically remove or vary components to understand contributions Scaling Analysis - Test how performance changes with model size, data size, or compute Transfer Learning - Adapt pretrained models to new tasks with appropriate fine-tuning strategies Ensemble Methods - Combine multiple models for improved and more robust performance
This skill connects with the Research-Claw ecosystem:
