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Get Started Free →Cleans messy CSV files into a normalized schema. Use this when the user has a .csv with malformed rows, inconsistent headers, or junk lines and wants a tidy spreadsheet out. Not for Excel-specific formatting.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | -69% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -63% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -34% | 0% |
Use this when the user provides a messy CSV and wants it normalized.
python scripts/normalize.py to parse the CSV and deduplicate rowsdeterministically. If it exits nonzero, report the error and stop.
so the output contract is enforced, otherwise the downstream step will reject it.
about what counts as "junk" for their data.
See the format guide for column conventions.
Other measured skills in the registry, with their headline benchmark lift.