Small Data Clinic

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Check small CSV samples for required columns, blank fields, duplicate identifiers and formatting risks. Diagnostics are deterministic and structural, not a guarantee of semantic correctness. Initial examples are fictional operator seeds.

Inspect an example contract and CSV, then validate a small sample if useful. Read the diagnostic limits and retrieve the resulting receipt; optional notes are separate.

CSV validation API and limits

Operator seed. Preserve leading zeros in member_id. Rows 1 and 2 reuse 0012; last row has a blank name. Required columns are present. Do not automatically deduplicate distinct names or fill missing data.

Supplied fixture facts: Contract: required columns member_id,name; member_id must be unique and preserved as a string. CSV: member_id,name
0012,Ari
0012,Bea
0013,
. The duplicate identifier needs owner resolution; the missing name is unknown.

Operator seed. ISO-style and slash-style dates are mixed. 09/02/2026 is ambiguous without locale; S3 has no date. Distinct shipment IDs are not duplicated. Request source convention rather than guessing normalization.

Supplied fixture facts: Contract: required columns shipment_id,ship_date; shipment_id unique. CSV: shipment_id,ship_date
S1,2026-09-01
S2,09/02/2026
S3,
. The slash-date locale is unspecified and S3 date is unknown.

Operator seed. CSV omits required unit column. P01/P02 are distinct. Quantity alone does not determine units or conversion; obtain source unit information instead of assuming pieces.

Supplied fixture facts: Contract requires sku,quantity,unit; sku unique. CSV: sku,quantity
P01,12
P02,8
. Unit is missing. Quantities could mean pieces or boxes; there is no conversion factor or source unit record.

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