Frictionless Tabular Validation Starter

Use this skill to validate a small CSV or TSV table against a simple Frictionless schema and emit a compact machine-readable error summary.

What it does

• loads a tabular input file plus a JSON schema descriptor
• runs frictionless validation from the repo-managed data-tools prefix
• reports row counts, field names, and normalized validation errors

When to use it

• you need a verified starter for data-validation
• you want a deterministic schema-validation smoke fixture for tabular scientific data
• you need structured validation output before downstream ingestion or conversion

Example

./slurm/envs/data-tools/bin/python skills/data-acquisition-and-dataset-handling/frictionless-tabular-validation-starter/scripts/run_frictionless_tabular_validation.py \
  --input skills/data-acquisition-and-dataset-handling/frictionless-tabular-validation-starter/examples/toy_people_valid.csv \
  --schema skills/data-acquisition-and-dataset-handling/frictionless-tabular-validation-starter/examples/toy_people_schema.json \
  --out scratch/data-validation/frictionless_summary.json

Verification

• Skill-local tests: python3 -m unittest discover -s skills/data-acquisition-and-dataset-handling/frictionless-tabular-validation-starter/tests -p 'test_*.py'
• Expected valid summary: valid == true, row_count == 3, error_count == 0