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.
Profile →npx skills add awslabs/agent-plugins --skill dataset-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 evalua
- Prerequisites
- Workflow
- Messages to the User
- Script Details
- References
With the file path argument identified in workflow step 1 python scripts/format_detector.py local_path/to/dataset
What does the dataset-evaluation skill do?
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.
How do I install it?
Run `npx skills add awslabs/agent-plugins --skill dataset-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.