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Record Routing Quality Audit: String Normalisation + IQR Outlier Flags
x402
Record Routing 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 2026-10-15).
Runs three local analytical reports on record routing data — a quality audit, string normalisation, and IQR-based outlier detection — for up to 200 rows of accepted/rejected record counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied record routing records (accepted_records, rejected_records). 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 reports for up to 200 supplied rows: (1) a record quality audit highlighting missing, malformed, or inconsistent values; (2) a string normalisation report cleaning dataset_group and other text fields; and (3) IQR-based outlier flags identifying statistically anomalous rows in the chosen numeric field (accepted_records or rejected_records). All analysis is performed locally with no external lookups.
Example use cases
ETL pipeline data quality check
“I just pulled a batch of 50 rows from our record routing pipeline — can you run a quality audit, normalise the string fields like dataset_group, and flag any IQR outliers in the rejected_records column so I know which rows to investigate?”
Detecting anomalous rejection spikes
“We have daily routing records across several dataset groups for October 2026 — can you identify which rows have unusually high rejected_records values using IQR outlier detection and give me a full quality report?”
Pre-ingestion data validation for analytics
“Before I load these record routing rows into our data warehouse, can you run a string normalisation pass on the dataset_group names and do a quality audit plus IQR outlier check on the accepted_records field across all 120 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, self-contained, three-in-one analytical pass on record routing data — covering quality auditing, string normalisation, and IQR outlier detection — without any external API calls or live data lookups. Ideal for pre-ingestion validation, pipeline monitoring, or ad-hoc data quality checks on batches up to 200 rows at very low cost ($0.01 USDC for all three reports).
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds batch limit
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, date, accepted_records, rejected_records) — validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
- Invalid field enum value — must be 'accepted_records' or 'rejected_records'
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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