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Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Numeric Distribution
x402
Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Numeric Distribution 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).
Runs three analytical reports (record quality audit, string normalisation, numeric distribution) 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 + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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
Three independent analytical reports delivered together: (1) a record quality audit covering completeness, consistency, and structural validity of the supplied rows; (2) a string normalisation report covering the dataset_group and other string fields; and (3) a numeric distribution report with quantiles, spread, and summary statistics for the selected numeric field (duplicate_rows or total_rows). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Duplicate rate audit across dataset groups
“I have 50 rows of duplicate observation records split across two dataset groups, each with a date, duplicate_rows count, and total_rows count — can you run the record quality audit, string normalisation, and numeric distribution reports on them and focus the numeric analysis on duplicate_rows?”
Data pipeline deduplication health check
“Our ETL pipeline produces a daily table of duplicate_rows vs total_rows per dataset group — please run all three analytics reports (quality audit, string normalisation, numeric distribution) on this week's 7 rows so I can spot any anomalies in the total_rows field.”
Pre-merge dataset quality profiling
“Before we merge two datasets I want to profile their overlap metrics — I have 120 duplicate observation records with row IDs, dates, group labels, and duplicate/total row counts. Run the quality audit, string normalisation check, and numeric distribution summary on them, analysing the duplicate_rows field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-193?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 duplicate-observation records and need three complementary data quality reports in a single low-cost call ($0.01 USDC). It is ideal for auditing deduplication pipeline outputs, profiling string consistency in group labels, and understanding the numeric distribution of duplicate counts — all without any external API calls, predictions, or live lookups. Prefer it over single-report alternatives when you need the combined quality + normalisation + distribution view on the same dataset in one shot.
Side effects
- More than 200 rows submitted — request rejected, maxItems limit exceeded
- Missing required fields (row_id, dataset_group, date, duplicate_rows, or total_rows) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Empty rows array — minItems constraint violation
- field selector value not in enum ['duplicate_rows','total_rows'] — invalid input error
- Non-numeric values in duplicate_rows or total_rows beyond allowed null — type error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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