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Source Diversity Record Quality Audit, String Normalisation & Missing-Value Fill
x402
Source Diversity 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 2026-10-24).
Audits record quality, normalises strings, and fills missing values for source diversity data (source_count, outlet_count) across up to 200 rows, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Source diversity — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied source diversity records (source_count, outlet_count). 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 per call: (1) a record quality audit flagging completeness and consistency issues, (2) a string normalisation report for text fields like channel_group, and (3) an explicit missing-value fill report for the chosen numeric field (source_count or outlet_count) using the selected imputation method (zero, mean, or median). All processing is local with no external lookups.
Example use cases
Pre-reporting source diversity data cleanup
“I have a batch of 50 source diversity records with channel groups and daily source counts — some values are missing or have inconsistent string formatting. Can you run a full quality audit, normalise the strings, and fill the missing source_count values using median imputation?”
Outlet count missing value imputation
“Several outlet_count entries in my dataset are null. Can you fill those missing outlet_count values using mean imputation across these rows, and also give me a record quality audit while you're at it?”
News channel group string normalisation
“My news source dataset has messy channel_group labels with inconsistent capitalisation and spacing. Can you normalise those strings, audit the overall record quality, and fill any missing source_count fields with zeros for these 100 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-17-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit, string normalisation, and missing-value imputation on source diversity data (source_count, outlet_count) in a single low-cost call. It is best suited for small-to-medium batches (up to 200 rows) of news or media source diversity records where no external data lookup or prediction is needed — purely local analytical processing. Prefer alternatives if you need live data enrichment, predictive modelling, or processing more than 200 rows at once.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, channel_group, date, source_count, outlet_count) causes row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation failure
- Invalid enum values for field or method result in schema rejection
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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