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Legal Document Inventories — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Legal Document Inventories — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Runs three local analytical reports on legal document inventory data: a record quality audit, string normalisation, and a secondary-measure weighted mean calculation for document_count or reviewed_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Legal document inventories — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied legal document inventories records (document_count, reviewed_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit flagging data issues such as nulls, out-of-range values, or inconsistencies in the supplied rows; (2) a string normalisation report cleaning and standardising the document_group field values; (3) a secondary-measure weighted mean calculation for the selected numeric field (document_count or reviewed_count). All computed locally with no external lookups.
Example use cases
Legal review backlog quality check
“I have a table of 50 legal document inventory records with document groups and daily reviewed counts — can you run a record quality audit on them, clean up the group name strings, and give me a weighted mean of the reviewed_count field so I can see the average review rate weighted by document volume?”
Document group label normalisation
“Some of the document_group labels in my legal inventory data are inconsistently cased or have extra spaces. Can you normalise those strings across my 30 rows and also run a quality audit to flag any rows with missing or suspect values?”
Weighted mean for compliance reporting
“For our compliance dashboard I need a secondary-measure weighted mean of document_count across these legal inventory rows — there are 80 rows covering different document groups and dates. Can you compute that along with a quality audit and string normalisation pass?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-10-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three operations — record quality audit, string normalisation, and secondary-measure weighted mean — applied to legal document inventory data (document_count, reviewed_count) in a single cheap call. It is ideal for batch preprocessing up to 200 rows of legal document tracking data before downstream reporting or compliance workflows, and when no live external data, predictions, or professional legal assessment is required.
Side effects
- Rows array is empty or missing — returns validation error
- More than 200 rows supplied — rejected with row limit error
- Invalid date format (not YYYY-MM-DD) in any row — validation failure
- Unrecognised field value outside enum [document_count, reviewed_count] — schema error
- Null values in both numeric fields for all rows — weighted mean may be undefined or return NaN
- row_id missing or exceeds 80 characters — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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