Dataset Evaluation
AWS LabsOfficialPublished by AWS Labs
- Skill
- Review and audit
- Software development
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/awslabs/dataset-evaluation/downloadUnzip 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-startedskill 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.mdEvaluation 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.mdand 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/datasetReferences
scripts/format_detector.py— Self-contained format validation scriptreferences/strategy_data_requirements.md— Data format requirements per strategy
Files
- ATTRIBUTION.mdAdded by AutoGineer920 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer89 B
- SKILL.md4.9 KiB
- references/custom-scorer-evaluation-dataset-formats.md4.1 KiB
- references/strategy_data_requirements.md4.8 KiB
- scripts/format_detector.pyScript25.0 KiB
LICENSE
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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
What our scanner noticed· Low risk
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
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)
- Synced
AutoGineer is not affiliated with or endorsed by AWS Labs or AWS Labs.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dataset-evaluation.zip