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Dataset Evaluation

AWS LabsOfficialPublished by AWS Labs

  • Skill
  • Review and audit
  • Software development
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

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.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a systematic workflow to verify that your training or evaluation datasets meet the specific schema and formatting requirements for Amazon SageMaker model fine-tuning (SFT, DPO, RLVR) or custom scorer evaluation. It automates the detection of file formats and ensures compliance with the selected model family and training strategy, helping you identify potential issues before initiating expensive training jobs.

What it does

  • Locates and verifies access to local or S3-based datasets.
  • Detects file formats and validates them against strategy-specific requirements.
  • Checks schema compliance for training datasets (SFT, DPO, RLVR).
  • Validates evaluation datasets for Prime Math, Prime Code, or Custom Lambda scorers.
  • Identifies mismatches between dataset structure and the intended fine-tuning strategy.
  • Provides guidance on necessary transformations if the data format is invalid.

When to use it

  • Before starting any model fine-tuning job on SageMaker.
  • When you need to confirm if your custom dataset is correctly formatted for a specific training strategy.
  • When preparing evaluation datasets for custom scorers like Prime Math or Prime Code.
  • When you are unsure if your data meets the requirements for Nova or Open Weights models.

Advantages

  • Prevents failed training jobs by catching formatting errors early.
  • Ensures alignment between data structure and the chosen fine-tuning strategy.
  • Supports both training and evaluation dataset validation workflows.
  • Provides clear feedback on whether data is ready for production use.

Usage examples

“Is my dataset okay for SFT training?”
The agent will locate your dataset, verify it against the SFT schema for your selected model, and confirm if it is ready for training.
“Check my training data for a DPO job.”
The agent will validate your file structure to ensure it contains the required prompt, chosen, and rejected fields.
“I have my own data for Prime Math evaluation, can you check it?”
The agent will verify your dataset against the Prime Math schema, ensuring it contains the correct query and response fields.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/awslabs/dataset-evaluation/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip dataset-evaluation.zip -d .agents/skills

Documentation by AWS Labs

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

  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 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.

  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 per references/strategy_data_requirements.md

      • Evaluation datasets (for model evaluation) must match one of the SageMaker evaluation dataset formats.

      • 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."

  • 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

  • model-selection and finetuning-technique skills should have already determined the base model and fine-tuning strategy

  • 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:

# With the file path argument identified in workflow step 1
python scripts/format_detector.py local_path/to/dataset

References

  • scripts/format_detector.py — Self-contained format validation script

  • references/strategy_data_requirements.md — Data format requirements per strategy

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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Attribution and licence

Published by
AWS Labs
Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/sagemaker-ai/skills/dataset-evaluation · commit fbefb37
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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