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Duplicate Observations — String Normalisation + Daily Sum Series Analytics
x402
Duplicate Observations — String Normalisation + Daily Sum Series Analytics is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Analyzes duplicate observation records by performing string normalization and generating daily sum series reports for up to 200 rows across dataset groups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — String normalisation + Daily sum series: String normalisation + Daily sum series 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 independent analytical reports: (1) a string-normalized view of the supplied observation records (standardizing dataset_group and other string fields), and (2) a daily sum series aggregating the specified numeric field (duplicate_rows or total_rows) across all input rows by date. Results are computed locally from supplied data with no external lookups.
Example use cases
Daily duplicate rate monitoring
“I have a list of up to 150 dataset records with dates, group names, duplicate_rows counts, and total_rows counts spanning October 2026 — can you run the string normalization and daily sum series reports on the duplicate_rows field so I can see how duplicates are trending day by day?”
Data quality audit by group
“We've got two dataset groups, group-a and group-b, and I need to understand the daily duplicate row totals for each. Can you normalize the group labels and produce a daily sum series on total_rows for these 80 records I'm sending?”
Pre-pipeline dedup analysis
“Before I load this batch of observation records into our data warehouse, I want to understand the duplication landscape — can you normalize the dataset group names and give me a day-by-day sum of duplicate_rows across all 60 rows so I know where the hotspots are?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-027?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw duplicate observation records (with date, group, duplicate_rows, and total_rows fields) and need both a normalized string view and a daily aggregated time series in a single low-cost call. Ideal for data quality pipelines, deduplication audits, and monitoring duplicate trends over time without requiring an external database or live data connection. Best when batch size is ≤200 rows and you want two complementary analytical perspectives for $0.01.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, dataset_group, duplicate_rows, total_rows) on any row causes validation failure
- Date field not matching YYYY-MM-DD pattern results in schema rejection
- Numeric values outside ±1,000,000,000,000 range cause validation error
- Empty rows array (minItems:1 not met) returns error
- Invalid field enum value (not 'duplicate_rows' or 'total_rows') rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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