How to use the CSV Validator.
Validate both file structure and the expected schema. A well-formed CSV can still contain missing fields or values of the wrong type.
Make the workflow fit your task.
Parse the file using its stated delimiter and encoding before checking required headers, row widths and column rules. Return each issue with a row or record reference, the original value and the violated rule. Keep syntax and data-quality findings separate.
- What you provide
- CSV and required schema.
- What you get
- Malformed rows, type conflicts and missing required values.
See the input and the result.
Illustrative input and output · a teaching example, not a live WebAct run
Example input
CSV schema: id is required; quantity must be a nonnegative integer. Header is record 1. id,quantity A1,2 A2,many
Completed example
Structure: two columns per record; required headers present. Record 3, id A2, quantity "many": fails nonnegative-integer rule. Record 2, id A1, quantity 2: passes. Original values retained; one record needs correction.
Load this input into the prompt, then copy it to WebAct to try the task. Your result may differ from the illustration.
Decisions and troubleshooting.
Does valid CSV syntax mean every row satisfies my schema?
No. A file can parse correctly while containing invalid dates, missing keys or text in numeric fields.
Why does a quoted address appear to create extra columns?
A parser must respect quoted delimiters and embedded line breaks. Splitting each line on commas is not sufficient.
Try it with your own source.
Replace the example with your material in the task prompt. Keep the requirements you need, then copy the task into WebAct.
Customize and copy the task ↑