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Job Listing Inventories — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Job Listing Inventories — Record Quality Audit, String Normalisation & Keyed Dataset Comparison 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 local analytical reports on supplied job listing inventory data: a record quality audit, string normalisation, and keyed dataset comparison between two input arrays (up to 200 rows each).
Price / call$0.01
Success—
StatusUnknown
How this service works
Job listing inventories — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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
Returns three independent analytical reports: (1) a record quality audit flagging nulls, out-of-range values, and structural issues in the supplied rows; (2) a string normalisation report identifying and correcting inconsistencies in text fields like company_group; and (3) a keyed dataset comparison report showing row-level differences between the primary (rows) and comparison (other) datasets, matched by row_id. All processing is local with no external lookups.
Example use cases
Pre-pipeline data quality gate
“Before I load this week's job listing inventory into our data warehouse, can you run a quality audit and string normalisation on these 150 rows — fields are row_id, company_group, date, listing_count, and duplicate_listings — and flag anything that looks off?”
Snapshot diff between two reporting periods
“I have two snapshots of our job listing inventory from October — one from the 1st and one from the 15th, each about 80 rows. Can you do a keyed comparison between them so I can see which records changed their listing_count or duplicate_listings values?”
Detecting duplicate inflation across company groups
“We think some company groups are inflating their duplicate_listings numbers. Can you run a record quality audit and keyed dataset comparison on these two CSV exports — about 120 rows each — and tell me which rows look suspicious?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-13-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, offline, deterministic quality check on job listing inventory data without any external API calls or live data lookups. It is ideal when you have two datasets to compare by key, need string normalisation on company names, and want a bundled 3-report output for a flat fee of $0.01 USDC. Not suitable for live job board queries, predictions, or datasets exceeding 200 rows.
Side effects
- Payload exceeds 200-row limit — request rejected
- Missing required fields (row_id, company_group, date, listing_count, duplicate_listings) — validation error
- Date fields not matching YYYY-MM-DD pattern — schema rejection
- Empty rows array — minItems:1 constraint violation
- Payment not attached or insufficient — x402 payment required error
- Mismatched row_id keys between rows and other arrays — comparison report may show all rows as unmatched
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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