dataset-transformation
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.
Profile →npx skills add awslabs/agent-plugins --skill dataset-transformation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 s
- When to Use
- Prerequisites
- Principles
- Known Dataset Formats Reference
- Training Data Formats
- Evaluation Data Formats
- Workflow
- Step 1: Determine transformation purpose
- Step 2: Set expectations
- Step 3: Understand the dataset transformation task
- Step 4: Get the dataset from the user
- Step 5: Examine sample data
- Step 6: Get the dataset output location
- Step 7: Generate and validate the transformation function
What does the dataset-transformation skill do?
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.
How do I install it?
Run `npx skills add awslabs/agent-plugins --skill dataset-transformation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.