Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
metadata
{"version":"1.0.0"}
Workflow Instruction
Follow the workflow shown below. Locate the dataset, check the file type, and resolve any issues with missing files or wrong file types. Determine the fine-tuning model and fine-tuning strategy. Run the appropriate validation based on the model family. Summarize the results: is the dataset ready for fine-tuning?
Prerequisites
The SDK environment has been verified (SDK version, region, execution role). If not done, activate the sdk-getting-started skill first.
Workflow
Locate Dataset:
The full path may be a local file path, or an S3 URI
Resolve the full path to the dataset file, make sure read permissions are available, and help the user if the file is not found
Determine strategy and model:
File formatting depends on the currently selected fine-tuning strategy and fine-tuning base model.
If the strategy and model are already known from the conversation context (e.g., selected via the model-selection and finetuning-technique skills), use them.
If not available in context, activate the model-selection and/or finetuning-technique skills to determine them before proceeding.
Exception: If the user is validating an evaluation dataset (not a training dataset), neither model nor technique is required — the format detector can validate eval format (query/response structure) independently. Do not block on model-selection or finetuning-technique for eval dataset validation.
Check File Formatting: Run the tool format_detector.py to make sure the file conforms to formatting requirements.
Send the full path directly to the format_detector script as an argument
Do not send the model and strategy as arguments
Do not download data from S3
Do not make local copies of data
Summarize Results: Tell the user if their data is ready
Examine the output of format_detector and compare to the known strategy and model
Important: training datasets and evaluation datasets have different format requirements.
Training datasets must match the fine-tuning strategy format per references/strategy_data_requirements.md
Custom Scorer evaluation datasets have scorer-specific requirements. If the dataset is intended for Custom Scorer evaluation (Prime Math, Prime Code, or Custom Lambda), read references/custom-scorer-evaluation-dataset-formats.md and validate against the scorer-specific schema. The scorer type should be known from conversation context (determined in the model-evaluation skill).
Report back to the user if their current dataset is valid for its intended purpose
Warn the user if their dataset is valid, but for a different strategy or model
Warn the user if their dataset is not valid for any strategy/model pair
If the user plans to finetune a model with the evaluated dataset, it needs to be uploaded to an S3 bucket in the same region as the planned training job (usually the default region). Warn the user if this is NOT the case.
If the dataset is NOT in the necessary format, recommend transforming it using the dataset-transformation skill, wait for user confirmation, and update the plan based on their response
Messages to the User
Introduction: "This skill checks the structure of your dataset for model fine-tuning."
