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Job Listing Inventories — Record Quality Audit + Explicit Missing-Value Fill
x402
Job Listing Inventories — Record Quality Audit + Explicit 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 2026-10-23).
Audits record quality and fills missing values in job listing inventory data (listing_count, duplicate_listings) for up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Job listing inventories — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied job listing inventories records (listing_count, duplicate_listings). 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 reports: (1) a record quality audit identifying data completeness issues, invalid values, or anomalies across the supplied rows; (2) an explicit missing-value fill report where nulls in the specified field (listing_count or duplicate_listings) are replaced using the chosen method (zero, mean, or median). No external data is fetched; all analysis uses local analytical engines on the supplied rows only.
Example use cases
HR data pipeline quality check
“I need to validate my job listing inventory data before loading it into the dashboard — can you run a record quality audit on these 80 rows and flag any issues with listing_count or duplicate_listings?”
Missing listing count imputation
“Some of my job listing records have null listing_count values. Can you fill them in using median imputation across all rows and give me back the completed dataset?”
Duplicate listings data cleanup
“I have 150 rows of company job posting data and the duplicate_listings field has several missing entries — can you audit the data quality and impute the missing duplicate_listings values using the mean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-13-019?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of job listing inventory data (with listing_count and duplicate_listings fields) and need both a data quality audit and explicit null imputation in a single call. It is ideal for preprocessing pipelines where no live data lookup or external enrichment is needed — purely local, deterministic analysis on supplied data. Prefer alternatives if you need more than 200 rows, live job market data, predictive analytics, or enrichment from external sources.
Side effects
- Input array exceeds 200 rows — request rejected
- row_id missing or empty — validation error
- date field does not match YYYY-MM-DD pattern — schema error
- Unsupported imputation method enum value — schema rejection
- All rows have null values for target field — mean/median cannot be computed
- Fewer than 1 row provided — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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