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Data Reconciliation — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Data Reconciliation — Record Quality Audit + String Normalisation + Daily Sum Series 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).
Runs three analytical reports on supplied data reconciliation rows: a record quality audit, string normalisation, and a daily sum series for source_total or target_total values.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series 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
Returns three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed fields; (2) a string normalisation report standardising dataset_group and other string fields; and (3) a daily sum series aggregating the selected numeric field (source_total or target_total) by date across the supplied rows.
Example use cases
Pre-pipeline reconciliation quality check
“I have 120 reconciliation rows with source and target totals by date and dataset group — can you run the record quality audit, string normalisation, and daily sum series on them so I can catch data issues before loading into the warehouse? Analyse the source_total field.”
Daily finance reconciliation summary
“Run a daily sum series plus record quality audit on these 80 rows of finance reconciliation data grouped by region — I want to see how target_total adds up day by day and whether there are any quality problems.”
ETL data validation for dataset groups
“Before I merge these two datasets, can you do a string normalisation and record quality audit on my 40 reconciliation rows? The dataset_group names might be inconsistent and I need to know if any source_total values are missing or malformed.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three complementary data-quality and summarisation reports on reconciliation data in a single low-cost call ($0.01 USDC). It is ideal for pre-pipeline validation workflows where you want quality auditing, string normalisation, and daily aggregation together without making separate API calls. Prefer it over general-purpose analytics tools when your data fits the reconciliation schema (source_total, target_total, date, dataset_group) and you need all three reports atomically on up to 200 rows.
Side effects
- Fewer than 1 row or more than 200 rows supplied — validation error
- date field does not match YYYY-MM-DD pattern — schema validation failure
- source_total or target_total outside ±1,000,000,000,000 — rejected
- row_id or dataset_group exceeds 80 characters — schema error
- field enum value not 'source_total' or 'target_total' — invalid input
- Payment not completed — 402 response, reports not generated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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