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Duplicate Observations Analytics — Record Quality Audit + String Normalisation + Grouped Numeric Summary
x402
Duplicate Observations Analytics — Record Quality Audit + String Normalisation + Grouped Numeric Summary 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 local analytical reports (record quality audit, string normalisation, and grouped numeric summary) on up to 200 duplicate-observation records containing duplicate_rows and total_rows fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied duplicate observations records (duplicate_rows, total_rows). 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 for the supplied rows: (1) a record quality audit flagging data issues, (2) a string normalisation report cleaning the dataset_group and other string fields, and (3) a grouped numeric summary of the selected field (duplicate_rows or total_rows) broken down by dataset_group. All analysis is performed locally with no external lookups.
Example use cases
Data pipeline deduplication audit
“I have a table tracking duplicate records across my data pipelines — each row has a date, a dataset group name, duplicate_rows, and total_rows. Can you run a record quality audit, normalise the group name strings, and give me a grouped numeric summary focused on duplicate_rows for these 50 rows?”
Monthly dataset group quality report
“I need a quality report on my dataset groups for October 2026. I have about 80 rows with fields: row_id, dataset_group, date, duplicate_rows, and total_rows. Please audit the record quality, normalise the dataset_group strings, and summarise total_rows grouped by dataset_group.”
Deduplication tracking sanity check
“Can you sanity-check my deduplication tracking data? I've got 20 rows — each has a row ID, a date, a group label, a count of duplicate rows, and total rows. Run the record quality audit, string normalisation, and a grouped numeric summary on duplicate_rows so I can spot any anomalies.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — record quality audit, string normalisation, and grouped numeric summary — on duplicate observation data in a single cost-efficient call ($0.01 USDC for all three). It is best suited for offline/batch analytical workflows where no live data lookup, prediction, or external action is required, and when your data fits within 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes rejection
- Invalid date format (non-YYYY-MM-DD) fails schema validation
- Empty rows array is rejected (minItems: 1)
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid field enum value (not 'duplicate_rows' or 'total_rows') fails validation
- Payment of 0.01 USDC not fulfilled causes 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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