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Rachasumanth
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data-annotation

Create, format, and validate instruction-tuning and preference datasets for AI model fine-tuning.

Updated Feb 17, 2026
Search Tags:#data validation#synthetic data#data annotation#instruction tuning#RLHF#dataset creation
One-click install
npx skills add https://github.com/Rachasumanth/text2llm001 --skill data-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-annotation
Source: https://github.com/Rachasumanth/text2llm001/tree/main/workspace/skills/data-annotation
Command: npx skills add https://github.com/Rachasumanth/text2llm001 --skill data-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires datasets, pandas, langdetect, regex, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the creation, formatting, and validation of datasets essential for training and aligning AI models, particularly for instruction tuning and reinforcement learning from human feedback (RLHF).

Core Features & Use Cases

  • Dataset Creation: Generate instruction-response pairs, multi-turn conversations, and preference datasets (chosen/rejected).
  • Synthetic Data Generation: Leverage LLMs to create synthetic training data when manual annotation is insufficient.
  • Quality Assurance: Implement validation checks for schema, duplicates, length, and toxicity.
  • Use Case: A machine learning engineer needs to create a dataset of customer support dialogues for fine-tuning a chatbot. This skill can help generate realistic conversations, format them correctly, and ensure the quality of the data before training.

Quick Start

Use the data-annotation skill to create 100 instruction-response pairs for summarization tasks.

Frequently Asked Questions about data-annotation

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

FAQPage Schema
How do I create preference datasets for RLHF alignment?▼

You can create preference datasets for RLHF alignment by generating chosen and rejected response pairs. This process formats DPO pairs correctly to facilitate reinforcement learning from human feedback.

What is the best way to format instruction tuning datasets for Alpaca and ShareGPT?▼

The best way to format instruction tuning datasets is by structuring instruction-response pairs and multi-turn conversations. This skill supports converting outputs into Alpaca, ShareGPT, and OpenAI chat formats.

Can I generate synthetic training data for fine-tuning chatbots?▼

Yes, you can generate synthetic training data for fine-tuning chatbots by leveraging LLMs. This approach creates realistic instruction-response pairs when manual annotation is insufficient for your dataset creation needs.

How do I validate dataset quality before AI model fine-tuning?▼

You validate dataset quality before AI model fine-tuning by running rigorous quality assurance checks. This skill validates schema, duplicates, length, and toxicity using dependencies like pandas, langdetect, and regex.

Does this dataset creation process support multi-turn conversations?▼

Yes, the dataset creation process supports multi-turn conversations for chatbot fine-tuning. It formats these interactions correctly while ensuring the quality of the data using validation checks.

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