dataset-validate

name	dataset-validate
description	Use this when the project needs a dedicated data-quality review before model review. Checks data reality, split correctness, label health, leakage risk, shape consistency, and mock-data disclosure.
metadata	{"openclaw":{"emoji":"🗂️","requires":{"bins":["python3","uv"]}}}

Dataset Validate

Don't ask permission. Just do it.

Use this skill before or alongside model implementation review when data quality needs to be checked separately from model quality.

Outputs go to the workspace root.

Use This When

• plan_res.md already exists
• the project is about to implement or has just implemented a model
• data quality, split quality, or label integrity is still uncertain

Do Not Use This When

• the project has no concrete plan yet
• there is no dataset or data-loading path to inspect

Required Inputs

• plan_res.md
• project/ if a data pipeline already exists
• survey_res.md when it defines dataset or protocol expectations

If plan_res.md is missing, stop and say: Run /research-plan first to complete the implementation plan.

Required Output

• data_validation.md

Workflow

Step 1: Read the Data Contract

Read:

• plan_res.md
• survey_res.md if present
• current data-loading code under project/data/ if present

Extract:

• expected dataset name
• source
• split structure
• label or target format
• expected shapes

Step 2: Audit Data Reality

Check:

• whether dataset files actually exist
• whether the data is real or mock
• whether mock usage is clearly declared
• whether row count / sample count is plausible

Step 3: Audit Data Integrity

Check:

• train / val / test split existence and separation
• label distribution or target sanity
• shape / dtype consistency
• obvious leakage risks
• preprocessing consistency with plan_res.md

If code exists, run lightweight inspection commands under the project environment to verify counts and sample structure.

Step 4: Write data_validation.md

Use references/data-validation-template.md.

The report must include:

• dataset identity
• data reality check
• split integrity
• label / target health
• leakage risk
• mock-data disclosure
• verdict: PASS, NEEDS_REVISION, or BLOCKED
• exact next step

Rules

1. Keep data quality separate from model quality.
2. Never infer that data is real if the files or loading path are missing.
3. If mock data is used, call it out explicitly.
4. If data leakage is plausible, treat it as blocking until clarified.