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dataset-evaluation

Skill

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

0Machine Learning

Install

git clone https://github.com/awslabs/agent-plugins.git ~/.claude/skills/dataset-evaluation

What is dataset-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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~100 tokens of context used while enabled, before you invoke anything

Documentation

README · ~3 min read

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:

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