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Transaction Fee Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Transaction Fee Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Runs three local analytical reports on transaction fee observation records: a record quality audit, string normalisation, and IQR-based outlier detection on a chosen numeric field.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transaction fee observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied transaction fee observations records (fee_units, transaction_count). 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 missing, invalid, or malformed fields; (2) a string normalisation report standardising text fields like instrument_group; (3) an IQR outlier flags report identifying numeric rows in the chosen field (fee_units or transaction_count) that fall outside the interquartile range bounds. All computed locally with no external lookups.
Example use cases
Pre-analysis data quality check
“I've got 120 rows of transaction fee observations with instrument_group, date, fee_units, and transaction_count — can you run a quality audit, normalise the string fields, and flag any IQR outliers in fee_units before I start my analysis?”
Detecting anomalous transaction counts
“Can you check my 80 transaction fee records for outliers in the transaction_count field, audit record quality, and normalise any messy strings in the instrument_group column?”
Cleaning fee data for reporting
“I need to clean up these 40 rows of daily fee observations before presenting them — please run a record quality audit, string normalisation, and IQR outlier detection on the fee_units values.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-03-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined quality-audit, string-normalisation, and IQR-outlier-detection pass on transaction fee observation data in a single call, without requiring any external data lookups or predictions. It is best suited for pre-analysis data cleaning workflows on small-to-medium batches (up to 200 rows) of financial fee records with fee_units and transaction_count fields.
Side effects
- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows submitted — exceeds maximum batch size
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, instrument_group, date, fee_units, transaction_count) causes validation error
- field enum value not one of fee_units or transaction_count
- Numeric values outside allowed range (-1e12 to 1e12) rejected
- IQR outlier detection may be unreliable with very few rows (fewer than 4)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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