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training-set-builder

Extract structured training examples from document sets into JSON datasets.

3|4|Updated Mar 11, 2026
Search Tags:#document analysis#natural language processing#instruction tuning#dataset creation#llm training#feedback extraction
One-click install
npx skills add https://github.com/nicsuzor/academicOps --skill training-set-builder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: training-set-builder
Source: https://github.com/nicsuzor/academicOps/tree/main/archived/skills/training-set-builder
Command: npx skills add https://github.com/nicsuzor/academicOps --skill training-set-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the extraction of structured training examples from various document sets, enabling the creation of high-quality datasets for teaching Large Language Models specific tasks or styles.

Core Features & Use Cases

  • Structured Data Extraction: Extracts source material, feedback, revised output, and contextual learning points into a defined JSON format.
  • Versatile Input Handling: Adapts to various document structures (paired, annotated, multi-version, custom).
  • Use Case: Process a collection of peer-reviewed articles to extract examples of constructive feedback and revisions, creating a dataset to train an LLM on providing effective critique.

Quick Start

Use the training-set-builder skill to extract training examples from the provided document collection.

Frequently Asked Questions about training-set-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an LLM training dataset from existing document revisions?▼

Structured training data extraction pulls source material, feedback, and revised output from documents into a JSON format. This process identifies contextual learning points to teach LLMs specific tasks or styles.

What is instruction tuning data extraction from peer-reviewed articles?▼

Instruction tuning data extraction from peer-reviewed articles identifies constructive feedback and revision patterns. It structures these interactions into datasets to train LLMs on providing effective critique.

Can I use custom document structures for LLM feedback extraction?▼

Custom document structures are supported for feedback extraction. The process adapts to paired, annotated, and multi-version document sets to identify learning patterns and structured examples.

What is the best way to create natural language processing datasets from revision histories?▼

Creating NLP datasets from revision histories involves analyzing document structures to extract source material and revised output. It requires clarifying learning goals to categorize extracted patterns accurately.

Do I need predefined document formats for instruction tuning dataset creation?▼

Predefined document formats are not required for dataset creation. The extraction process adapts to various custom structures, though careful analysis and user clarification are needed to define learning goals.

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