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Data Reconciliation: Adjacent Row Changes + Explicit Missing-Value Fill
x402
Data Reconciliation: Adjacent Row Changes + Explicit 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 2026-10-19).
Computes adjacent row-over-row changes and fills missing values (zero, mean, or median) in source_total/target_total reconciliation data, returning 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns 2 analytical reports per call: (1) an adjacent row-over-row change analysis showing deltas between consecutive rows for the chosen field, and (2) a missing-value fill report where null source_total or target_total entries are replaced using the specified method (zero, mean, or median). Results cover up to 200 input rows with no external lookups or predictions.
Example use cases
Finance team reconciliation gap detection
“I've got daily source and target totals for our finance reconciliation across two dataset groups from October 2026, with some nulls in source_total. Run the adjacent row changes report and fill missing source_total values using the median — I need to see where the numbers diverge.”
ETL pipeline data quality audit
“After our ETL run, we have 120 rows of source_total and target_total records, and some target_total values are missing. Can you compute row-over-row changes and fill those missing target_total entries using zero fill so I can audit the pipeline output?”
Monthly dataset group trend analysis
“I have reconciliation records grouped by dataset_group with dates spanning 30 days — a few source_total values are null. Please run the adjacent row changes analysis and fill the missing source_total values using the mean, so I can compare trends across groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-162?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both adjacent row change detection and missing-value imputation on reconciliation data (source_total vs target_total) in a single $0.01 call that yields 2 reports. Ideal for finance, ETL, or data quality workflows processing up to 200 dated rows grouped by dataset category, where no external data lookup or predictive modeling is needed — purely local analytical computation.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- source_total or target_total values outside ±1,000,000,000,000 range are rejected
- Missing required fields (row_id, dataset_group, date) cause validation errors
- Invalid method enum value (not zero/mean/median) causes rejection
- Invalid field enum value (not source_total/target_total) causes rejection
- Empty rows array (minItems:1 not met) returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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