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Data Reconciliation — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Data Reconciliation — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three local analytical reports on up to 200 reconciliation rows: a record quality audit, string normalisation, and a trailing mean 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 + Trailing mean series: Record quality audit + String normalisation + Trailing mean 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
Three independent analytical reports: (1) a record quality audit flagging issues such as nulls, format errors, or out-of-range values; (2) a string normalisation report cleaning dataset_group and similar text fields; (3) a trailing mean series computed over the specified window for the chosen field (source_total or target_total). All analysis is performed locally with no external lookups.
Example use cases
Financial reconciliation data quality check
“I have 120 rows of daily source and target totals from our finance system — can you audit their record quality, normalise the dataset_group strings, and compute a trailing mean series over a window of 5 for source_total?”
ETL pipeline output validation
“After our ETL run, I want to validate the output: run a record quality audit, string normalisation, and a trailing mean with window 7 on target_total across these 80 reconciliation rows with dates, row IDs, and group labels.”
Multi-group reconciliation anomaly review
“We have reconciliation records for two dataset groups — group-a and group-b — across 30 days. Can you audit the record quality, clean up any string inconsistencies, and show me a trailing mean series with window 4 for source_total?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytics reports — quality audit, string normalisation, and rolling mean — on reconciliation data with source and target totals, all in a single $0.01 call with no external data dependencies. Prefer it over separate single-purpose analytics tools when you want cost-efficient batch analysis of up to 200 reconciliation rows without requiring live lookups or predictions.
Side effects
- Fewer than 1 or more than 200 rows supplied — rejected with validation error
- Window size smaller than 2 or larger than 200 — rejected with validation error
- Date values not matching YYYY-MM-DD pattern — validation error on offending rows
- source_total or target_total values outside ±1,000,000,000,000 — rejected
- Invalid field enum value (not source_total or target_total) — schema validation error
- Missing required row fields (row_id, dataset_group, date) — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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