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Duplicate Observations — Record Quality Audit, String Normalisation & Duplicate Key Report
x402
Duplicate Observations — Record Quality Audit, String Normalisation & Duplicate Key Report 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 supplied duplicate-observation records to produce three local analytical reports: a record quality audit, string normalization output, and a duplicate key report — all without external lookups or live data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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 reports for the submitted rows (up to 200): (1) a record quality audit flagging data integrity issues, (2) a string normalization report cleaning and standardizing text fields like dataset_group, and (3) a duplicate key report identifying rows with matching or suspicious key combinations — all computed locally with no external calls.
Example use cases
ETL pipeline duplicate rate audit
“I just loaded 150 observation records into my pipeline — each has a row ID, dataset group, date, and duplicate and total row counts. Can you run all three reports (quality audit, string normalization, duplicate key report) on them so I can see where duplicates are concentrated and whether any group names need cleaning?”
Daily data quality check for grouped observations
“Every morning I want to audit yesterday's observation batch for duplicate keys and string inconsistencies. Here are 80 rows, each tagged with a row ID, a dataset group like 'group-a' or 'Group A', a date, and their duplicate_rows vs total_rows counts — run the full quality audit and normalization report.”
Pre-analysis deduplication report for a small dataset
“Before I run my analysis, can you check these 25 observation records for duplicate keys and normalize the dataset group strings? Each row has an ID, group name, date in YYYY-MM-DD format, and counts of duplicate rows versus total rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 pre-collected observation records with duplicate counts and need three bundled analytical reports (quality audit, string normalization, duplicate key detection) in a single low-cost call. It is ideal for lightweight, offline-style data quality checks on batch observation data where no live external lookup, prediction, or professional assessment is needed. It is not suitable for datasets exceeding 200 rows or for use cases requiring real-time streaming analysis or ML-based anomaly detection.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes validation errors
- date field not matching YYYY-MM-DD pattern causes schema rejection
- duplicate_rows or total_rows values outside allowed numeric range cause errors
- Empty rows array (minItems: 1 violated) causes rejection
- Payment not fulfilled — x402 payment required before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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