dataset-evaluation

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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.

3installs
Sourceawslabs/agent-plugins
Added on

NPX Install

npx skill4agent add awslabs/agent-plugins dataset-evaluation

Tags

Translated version includes tags in frontmatter
sagemakerdataset-validationfine-tuningdata-quality-checkai-training-data

SKILL.md Content

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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 scripts/format_detector.py to evaluate whether the file is formatted correctly for the currently selected model and strategy. Summarize the results: is the dataset ready for fine-tuning?

Workflow

  1. 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
  2. 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 finetuning-setup skill), use them.
    • If not available in context, activate the finetuning-setup skill to determine them before proceeding.
  3. 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
  4. 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 (SFT, DPO, RLVR) per 
        references/strategy_data_requirements.md
      • Evaluation datasets (for model evaluation) must match one of the SageMaker evaluation dataset formats.
    • 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

Messages to the User

  • Introduction: "This skill checks the structure of your dataset for model fine-tuning."
  • File types: This skill applies to files that are formatted according to the Amazon SageMaker AI Developer Guide

Resources

  • scripts/format_detector.py is self-contained format validation script that can be run independently
  • finetuning-setup skill should have already determined the fine-tuning strategy and base model
  • references/strategy_data_requirements.md contains data format requirements per strategy

Script Details

  • scripts/format_detector.py is self-contained format validation script that can be run independently:
bash
# With the file path argument identified in workflow step 1
python scripts/format_detector.py local_path/to/dataset