ai-modcon/genesis-skills/croissant-validator

Croissant Metadata Validator & Generator

Validate and generate croissant.json metadata for ML datasets.

Use This Skill When

• User asks to validate Croissant metadata.
• User asks to create or update croissant.json.
• User asks for MLCommons Croissant conformance checks.

Skill Layout

• scripts/validate_croissant.py: required-field checks + optional mlcroissant validation.
• scripts/generate_croissant_from_csv.py: generate metadata from CSV headers and sample values.

Keep executable code in scripts/; keep SKILL.md focused on workflow.

Python Environment Rule

For any command requiring package installs, use a temporary venv in the working directory and remove it after execution.

python3 -m venv .venv_croissant && \
.venv_croissant/bin/pip install -q mlcroissant pandas && \
# ... run command(s) ... && \
rm -rf .venv_croissant

Never run global pip install.

Validation Workflow

1. Locate metadata file (croissant.json, metadata.json, or user-provided path).
2. Run script-level validation:

``bash python3 skills/croissant-validator/scripts/validate_croissant.py path/to/croissant.json ``

1. If mlcroissant validation is required, run in venv:

``bash python3 -m venv .venv_croissant && \ .venv_croissant/bin/pip install -q mlcroissant && \ .venv_croissant/bin/python skills/croissant-validator/scripts/validate_croissant.py path/to/croissant.json && \ rm -rf .venv_croissant ``

1. Report: pass/fail, missing required fields, structural warnings, and fix suggestions.

Generation Workflow (CSV)

1. Collect required inputs:

• dataset name
• description
• SPDX license URL
• creator name
• dataset URL

1. Generate metadata from CSV:

``bash python3 skills/croissant-validator/scripts/generate_croissant_from_csv.py \ data.csv \ "My Dataset" \ "Dataset description" \ "https://spdx.org/licenses/MIT.html" \ "Creator Name" \ "https://example.com/dataset" \ --output croissant.json \ --cite-as "@misc{my-dataset-2026, title={My Dataset}, author={Creator Name}, year={2026}}" ``

1. Validate generated file with validate_croissant.py.
2. If needed, rerun generation or patch metadata manually.

Required Metadata Fields

• @context
• @type = sc:Dataset
• conformsTo = http://mlcommons.org/croissant/1.0
• name
• description
• license
• url
• creator
• datePublished
• distribution

High-Value Checks

• All @id values are unique.
• Each cr:FileObject has sha256 or md5.
• recordSet/field references are valid.
• Dates use ISO format (YYYY-MM-DD).
• License uses SPDX URL.

Common Fixes

• Add missing conformsTo.
• Replace non-SPDX license values with SPDX URLs.
• Add checksums to cr:FileObject entries.
• Add missing field.source mappings.

Resources

• Spec: https://docs.mlcommons.org/croissant/docs/croissant-spec.html
• GitHub: https://github.com/mlcommons/croissant
• Release: http://mlcommons.org/croissant/1.0