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Data Reconciliation: Record Quality Audit + String Normalisation + Missing-Value Fill
x402
Data Reconciliation: 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 2026-10-15).
Audits record quality, normalises strings, and fills missing values for up to 200 source/target reconciliation rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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 issues like nulls, malformed dates, or out-of-range values; (2) a string normalisation report standardising dataset_group and other string fields; (3) a missing-value fill report with imputed values for the chosen field (source_total or target_total) using zero, mean, or median strategy. All computed locally from the supplied rows — no external data sources.
Example use cases
Finance team reconciliation quality check
“I have 50 reconciliation rows from last month with row IDs, dataset groups, dates, and source and target totals — some source_total values are null. Can you audit their record quality, normalise the dataset group strings, and fill those missing source_total values using the median?”
ETL pipeline data validation before load
“Before we load these 120 reconciliation records into the warehouse, can you run a quality audit, normalise all string fields, and fill any missing target_total values using zero imputation? I'll send you the rows with row IDs, groups, and dates.”
Data analyst spotting reconciliation anomalies
“I've got 30 rows of reconciliation data with source and target totals — some values look off or blank. Can you give me a record quality audit, string normalisation, and mean-based fill for the missing source_total fields so I can see what needs fixing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined data reconciliation reports (quality audit, string normalisation, and missing-value imputation) in a single $0.01 call on up to 200 rows of source/target data. Ideal for pre-load validation, finance reconciliation QA, or ETL pipeline hygiene checks where you want offline, local analytics without any external lookups or predictions.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) per row — schema validation failure
- Date field not in YYYY-MM-DD format — pattern validation error
- Numeric values outside [-1,000,000,000,000, 1,000,000,000,000] range — schema rejection
- Invalid imputation method or field enum value — schema error
- Empty rows array — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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