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Job Listing Inventories — Record Quality Audit, String Normalisation & Missing-Value Fill
x402
Job Listing Inventories — Record Quality Audit, String Normalisation & Missing-Value Fill 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).
Audits job listing inventory records for quality, normalises strings, and fills missing values for up to 200 rows across 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Job listing inventories — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied job listing inventories records (listing_count, duplicate_listings). 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 covering: (1) a record quality audit highlighting issues such as missing values, formatting errors, and duplicates; (2) string normalisation details showing corrected or standardised company group and date strings; and (3) explicit missing-value fills for the chosen field (listing_count or duplicate_listings) using the selected imputation method (zero, mean, or median). All reports are generated locally without live lookup or external data access.
Example use cases
Audit recruiter platform listing data
“I have a CSV export from our recruiter platform with up to 150 rows of job listing counts and duplicate listing figures by company group and date — can you run a record quality audit, normalise the strings, and fill any missing listing_count values using median imputation?”
Clean HR analytics dataset before reporting
“Before I send our monthly HR analytics report, can you clean up our job listing inventory records? There are about 80 rows with company_group, date, listing_count, and duplicate_listings — please audit quality, normalise strings, and fill missing duplicate_listings values using the mean method.”
Pre-process listing inventory for data pipeline
“I need to pre-process our job listing inventory snapshot for the data pipeline — run a quality audit, string normalisation, and zero-fill any missing listing_count values across these 60 rows grouped by company and date.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-13-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of job listing inventory data (listing_count, duplicate_listings) and need a combined data quality audit, string normalisation, and missing-value imputation in a single low-cost call ($0.01 USDC). It is ideal for pre-processing job listing datasets before analytics or reporting, especially when no live external data lookup or prediction is required and all processing can be done locally.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level validation errors
- Missing required fields (row_id, company_group, date) cause row rejection
- Numeric values outside the allowed range trigger schema validation errors
- Empty rows array returns an error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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