Dataset Transformation
Generates code that transforms datasets between ML schemas for model training or...
Install
npx skills add awslabs/agent-plugins --skill dataset-transformation -g -a claude-code -y- Repository
- awslabs/agent-plugins933
- Category
- Data and ML
- Updated
- 20d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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-startedskill first.
Principles
- One thing at a time. Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn.
- Confirm before proceeding. Wait for the user to agree before moving to the next step. You are a guide, not a runaway train.
- 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.
- 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.
- No repetition. If you said something before a tool call, don't repeat it after. Only share new information.
- 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!
- 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.
- 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).
