ADu2021/can-llms-clean-up-your-mess-a-survey-of-applicatio
skills/skillxiv-v0.0.2-claude-opus-4.6/can-llms-clean-up-your-mess-a-survey-of-applicatio/SKILL.md
Implement techniques from Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs. Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications
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SKILL.md
- name:
- can-llms-clean-up-your-mess-a-survey-of-applicatio
- title:
- Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs
- version:
- 0.0.2
- engine:
- skillxiv-v0.0.2-claude-opus-4.6
- license:
- MIT
- url:
- https://arxiv.org/abs/2601.17058
- keywords:
- ["research", "methodology"]
- description:
- Implement techniques from Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs. Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications
Overview
This skill implements concepts from the research paper [2601.17058].
When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
Key Concepts
The paper addresses: Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e....
For detailed methodology, refer to the full paper.
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