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Research Task Completion — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Research Task Completion — Record Quality Audit + String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Audits record quality, normalises strings, and produces a date-by-category pivot for up to 200 research task completion rows (completed_tasks, assigned_tasks) in three independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Research task completion — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied research task completion records (completed_tasks, assigned_tasks). 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: (1) a record quality audit flagging issues such as nulls, duplicates, and out-of-range values; (2) a string normalisation report for the company_group field; and (3) a date-by-category pivot table summarising the chosen numeric field (completed_tasks or assigned_tasks) by date and company group — all derived from the supplied data without any external lookups.
Example use cases
Quality audit on task completion dataset
“I have a batch of research task completion records with company groups and dates — can you audit them for record quality issues, normalise the company_group strings, and pivot completed_tasks by date and category? I'll send you up to 100 rows.”
Pivot assigned tasks by team and date
“Take my research task dataset — it has row_id, date, company_group, assigned_tasks, and completed_tasks columns — and give me a date-by-category pivot focusing on the assigned_tasks field, plus a string normalisation and quality audit report.”
Pre-reporting data clean for task metrics
“Before I build my weekly task metrics report, can you run a quality audit, clean up the company_group names, and produce a date-by-category breakdown of completed_tasks for the 80 rows I'm going to send you?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-22-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and date-by-category pivot — on research task completion data in a single low-cost call. It is ideal for pre-processing task datasets before reporting, when you have up to 200 rows and want offline/local analysis without any external data lookups or live predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, company_group, date) triggers schema validation failure
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Invalid field enum value (not completed_tasks or assigned_tasks) causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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