tokenization
🔌PluginOrchestra-Research/AI-research-SKILLs
|A tokenization skill from the AI Research Engineering Skills Library, which offers 83 skills across 20 categories covering model architecture, fine-tuning, inference, and other AI research areas.
Overview
Tokenization is a skill from Orchestra Research's AI Research Engineering Skills Library, the most comprehensive open-source collection of AI research engineering skills for AI coding agents. It is one of 83 skills organized across 20 categories that enable coding agents to write and conduct AI research experiments, including preparing datasets, executing training pipelines, and deploying models.
Key Features
- Tokenization Expertise - Part of the dedicated Tokenization category within the library, providing specialized knowledge for text tokenization tasks in AI/ML pipelines
- 83 Skills Across 20 Categories - Comprehensive coverage spanning model architecture, fine-tuning, post-training, distributed training, optimization, inference, data processing, evaluation, safety, RAG, multimodal, and more
- Research Agent Building Blocks - Skills serve as the engineering ability layer that enables coding agents to conduct AI research experiments end-to-end
- NPM Distribution - Installable via npm as
@orchestra-research/ai-research-skillsfor easy integration into existing workflows - Open Source & Community - MIT licensed with an active Slack community, designed for collaboration and extension by AI researchers
Who is this for?
This skill is designed for AI researchers and ML engineers who want their coding agents to handle tokenization tasks within research workflows. It is ideal for teams building AI research pipelines who need agents capable of preparing text data, configuring tokenizers, and integrating tokenization steps into larger training and inference workflows.
Part of
orchestra-research-ai-research-skills
Installation
/plugin marketplace add orchestra-research/AI-research-SKILLs/plugin install tokenization@ai-research-skillsMore from this repository10
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