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Duplicate Observations — String Normalisation + IQR Outlier Flags
x402
Duplicate Observations — 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 2026-10-20).
Runs string normalisation and IQR-based outlier detection on up to 200 duplicate-observation records (duplicate_rows, total_rows), returning two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied duplicate observations records (duplicate_rows, total_rows). 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
Two analytical reports per call: (1) a string normalisation report cleaning and standardising text fields in the supplied records, and (2) an IQR outlier flag report identifying statistically anomalous values in the selected numeric field (duplicate_rows or total_rows) using interquartile range analysis. Results are based entirely on the supplied data with no external lookups.
Example use cases
Daily data pipeline duplicate audit
“Here are 120 records from our daily ingestion pipeline, each with a dataset_group, date, duplicate_rows, and total_rows. Can you normalise the string fields and flag any IQR outliers in the duplicate_rows column so I can spot which days had abnormal duplication?”
Cross-group dataset quality check
“I've got 80 observations across several dataset groups with their duplicate_rows and total_rows counts. Please run the IQR outlier flags on total_rows and also normalise the string fields — I want to see which groups are statistical anomalies.”
Historical duplicate trend anomaly detection
“We have 6 months of weekly duplicate_rows and total_rows readings per dataset group. Can you apply string normalisation and IQR outlier detection on the duplicate_rows field across all 200 rows and return the two analytical reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and IQR-based outlier detection on duplicate observation records in a single low-cost call. It is ideal for data quality pipelines that track duplicate_rows and total_rows metrics across dataset groups over time, and when you need two independent analytical reports from a single batch of up to 200 rows. Prefer it over general-purpose analytics tools when your input is specifically structured duplicate-observation data with dataset_group, date, and row count fields.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes schema rejection
- Dates not matching YYYY-MM-DD pattern fail validation
- Supplying fewer than 1 row triggers minItems constraint error
- Numeric values outside ±1,000,000,000,000 fail schema validation
- Invalid field enum value (anything other than duplicate_rows or total_rows) rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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