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Specification Traceability — Record Quality Audit + String Normalisation + Missing-Value Fill
x402
Specification Traceability — Record Quality Audit + String Normalisation + Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Audits record quality, normalises strings, and fills missing values in specification traceability data (mapped_requirements, total_requirements) for up to 200 rows, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Specification traceability — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied specification traceability records (mapped_requirements, total_requirements). 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 anomalies and inconsistencies in the traceability rows, (2) a string normalisation report showing standardised document_group and other text fields, and (3) a missing-value fill report where null or absent mapped_requirements or total_requirements values have been explicitly imputed using the chosen method (zero, mean, or median). All processing is performed locally with no external lookups.
Example use cases
Pre-release requirements coverage cleanup
“Before our release audit, can you run a quality check on our traceability records? I'll send you 120 rows with row_id, document_group, date, mapped_requirements, and total_requirements — fill missing total_requirements values using the mean, and give me all three reports: quality audit, string normalisation, and the filled values.”
Compliance data validation for a document group
“I need to validate the traceability data for our compliance submission. Here are 80 rows of spec records with mapped and total requirements counts — please audit data quality, normalise the string fields, and fill any missing mapped_requirements using zero imputation.”
Identifying data gaps before a requirements review
“We have a requirements review meeting coming up and I'm worried there are missing or inconsistent values in our traceability spreadsheet. Can you take these 35 rows and produce the quality audit, string normalisation, and median-filled missing-value reports for the mapped_requirements field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-21-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data-quality audit, string normalisation, and missing-value imputation pass on specification traceability records in a single low-cost call. It is best suited for batch validation of up to 200 rows of requirements-mapping data before compliance reviews, release audits, or reporting pipelines, and when no live external data lookup or predictive modelling is needed.
Side effects
- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, date) are rejected
- Dates not matching YYYY-MM-DD pattern cause schema validation failure
- Invalid method enum value (outside zero/mean/median) returns an error
- Invalid field enum value (outside mapped_requirements/total_requirements) returns an error
- Numeric values outside the allowed range cause rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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