Generates code that transforms datasets between ML schemas for model training or evaluation.
SkillScore64Maintained
Automatic
◆AI Engineer pack
Last verified 8/16/2026
What it changes
Passed 16 of 25 test cases with the skill, and 3 of 25 without it.
Without skill12%
With skill64%
Checks it passed that the model alone missed
Ends the response with a question.
Imports transform_dataset from transform_fn.
Uses .jsonl as the default output file extension.
and 18 more in the full report
13 fixed3 passed either way9 still failing0 broke
Pass-rate lift
+52 pp
Tokens
+101%
Agent turns
0%
Response time
-67%
Model gemini-3.6-flashMeasured 8/16/2026v116 of 25 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 13
Prompt
I have a dataset in ./data/input.json that needs conversion. I haven't specified whether it's for model training or model evaluation. What is the immediate first question to ask?
What was checked, without and with the skill
Checked
Without skill
With skill
Asks whether the dataset transformation is for model training or model evaluation.
Missed
Passed
case-19 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md228 lines5 filesincludes scripts
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
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).
Known Dataset Formats Reference
This skill supports two transformation purposes — training data and evaluation data — each with its own format resolution path. The purpose is determined in Step 1 of the workflow.
Training Data Formats
Resolve the target format using the reference file ../dataset-evaluation/references/strategy_data_requirements.md. When the transformation is for model training, the required format depends on both the model type (Open Weights like Llama/Qwen vs Nova) and the finetuning technique (SFT, DPO, RLVR, RLAIF) — make sure to match on both dimensions. If either the model type or technique is not yet known, ask the user before resolving the format.
Evaluation Data Formats
When the transformation is for model evaluation, resolve the target format using this order:
If the fetch fails (e.g., no internet access, VPC environment), fall back to the offline copy at references/sagemaker_dataset_formats.md. Inform the user that the format schemas are from an offline copy and may be outdated.