# Dataset Transformation

Generates code that transforms datasets between ML schemas for model training or...

- Repository: https://github.com/awslabs/agent-plugins (933 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-15
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add awslabs/agent-plugins --skill dataset-transformation -g -a claude-code -y`
Codex: `npx skills add awslabs/agent-plugins --skill dataset-transformation -g -a codex -y`
Cursor: `npx skills add awslabs/agent-plugins --skill dataset-transformation -g -a cursor -y`
Gemini CLI: `npx skills add awslabs/agent-plugins --skill dataset-transformation -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add awslabs/agent-plugins`

## Description

Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format, always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Dataset Transformation Agent

Transforms a data set provided by the user into their desired format.

## When to Use

- User needs to generate code for transforming datasets for SageMaker model training or model evaluation.
- A dataset requires processing, cleaning, or formatting before training or evaluation.
- Workflow requires a formal review and approval cycle before execution.

## Prerequisites

- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the `sdk-getting-started` skill first.

## Principles

1. **One thing at a time.** Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn.
2. **Confirm before proceeding.** Wait for the user to agree before moving to the next step. You are a guide, not a runaway train.
3. **Don't read files until you need them.** Only read reference files when you've reached the workflow step that requires them and the user has confirmed the direction. Never read ahead.
4. **No narration.** Don't explain what you're about to do or what you just did. Share outcomes and ask questions. Keep responses short and focused.
5. **No repetition.** If you said something before a tool call, don't repeat it after. Only share new information.
6. **Do not deviate from the Workflow.** The steps listed in the workflow should be followed exactly as described. Progress from Step 1 to Step 11 to complete the task. Do not deviate from the workflow!
7. **Always end with a question.** Whenever you pause for user input, acknowledgment, or feedback, your response must end with a question. Never leave the user with a statement and expect them to know they need to respond.
8. **Default output format is JSONL.** Unless the user explicitly requests a different file format, the transformed dataset should be written as `.jsonl` (JSON Lines, one JSON object per line).

## Known Dataset Formats Reference

The rest: https://github.com/awslabs/agent-plugins/blob/HEAD/plugins/sagemaker-ai/skills/dataset-transformation/SKILL.md