Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
arXiv:2607.07504v1 Announce Type: new Abstract: Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests, and formatting results. Reusable skill files are meant to avoid prompting from scratch by packaging guidance for a task family. Expert-written skills can encode high-quality guidance, but writing and maintaining them across many data-science task families creates a manual bottleneck. We ask whether LLM-generated skills offer a useful low-curation alternative: do they improve performance over the
The proliferation of LLM-based agents in enterprise workflows necessitates evaluating their efficiency gains, especially in data science, making this research timely.
Organizations can potentially automate high-value, recurring data science tasks more efficiently through LLM-generated skills, increasing productivity and reducing human effort.
The reliance on manually curated expert skills for LLM agents could decrease, shifting towards more scalable, LLM-generated solutions for task automation.
- · AI software providers
- · Data science teams
- · Product management
- · Low-skilled data analysts
- · Manual script writers
Companies will explore integrating LLM-generated skills to streamline data-science operations.
The demand for 'prompt engineering' specialists focusing on skill generation might rise, alongside the demand for those who can validate and refine LLM-generated code.
This could lead to a broader adoption of autonomous AI agents across white-collar professions, accelerating workflow automation.
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Read at arXiv cs.AI