Skill Detail

Generate LLM fine-tuning, RAG, and eval datasets from source material with easy-dataset

Turn raw documents into structured fine-tuning, RAG, and evaluation datasets when the real job is dataset preparation, not generic document parsing.

Data Extraction & TransformationMulti-Framework
Data Extraction & Transformation Multi-Framework Security Reviewed
⭐ 14k GitHub stars
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill generate-llm-fine-tuning-rag-and-eval-datasets-from-source-material-with-easy-dataset Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
easy-dataset application, supported source documents such as PDF/Markdown/DOCX/TXT/EPUB, and an operator or agent preparing datasets
Install & setup
Install or run easy-dataset from the upstream GitHub project, then load source documents and use its dataset-building flows to generate fine-tuning, RAG, or evaluation datasets.
Author
ConardLi
Publisher
GitHub Repository
Last updated
Apr 16, 2026
Quick brief

Use easy-dataset when an agent needs to transform raw source material into publishable training, retrieval, or evaluation datasets. The skill boundary is tight: ingest mixed documents, segment them intelligently, clean and structure the outputs, then produce artifacts ready for fine-tuning, RAG ingestion, or benchmark-style eval work. This is not a generic LLM platform listing. The value is the operator workflow of converting messy inputs into usable dataset assets at the point where a team would otherwise hand-roll parsing, chunking, and curation steps for each project.