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Source Diversity Analytics: Record Quality Audit, String Normalisation & Two-Measure Correlation
x402
Source Diversity Analytics: Record Quality Audit, String Normalisation & Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-29).
Audits record quality, normalises strings, and computes correlation between source_count and outlet_count for up to 200 rows of source diversity data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Source diversity — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation 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
Returns three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed dates in the supplied rows; (2) a string normalisation report identifying and correcting inconsistencies in channel_group and other string fields; and (3) a two-measure correlation report quantifying the statistical relationship between source_count and outlet_count across all supplied rows. All processing is local with no external lookups.
Example use cases
Media dataset quality audit
“I have 150 rows of daily news source diversity data with channel groups, dates, source counts, and outlet counts — can you audit the record quality, normalise any inconsistent channel group strings, and tell me how correlated source_count is with outlet_count across the whole dataset?”
Outlet coverage correlation check
“Run a two-measure correlation between source_count and outlet_count on my dataset of 80 media records spanning different channel groups and dates — I also want the record quality audit and string normalisation reports included.”
Pre-reporting data validation for source diversity
“Before I publish my monthly source diversity report, can you check my 60 rows for record quality issues, normalise the channel group labels, and compute the correlation between sources and outlets? The data covers October and November 2026 across groups like 'print', 'digital', and 'broadcast'.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-17-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-efficient trio of analytical operations — record quality audit, string normalisation, and two-measure correlation — on source diversity data (source_count, outlet_count) in a single API call. It is ideal for media analysts, journalism researchers, or data engineers who want to validate and understand relationships in news source diversity datasets without building separate pipelines. Best when data fits within 200 rows and no external lookups or predictions are required.
Side effects
- More than 200 rows submitted — request rejected with maxItems violation
- Missing required fields (row_id, channel_group, date, source_count, outlet_count) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- source_count or outlet_count outside allowed numeric range — validation error
- Empty rows array (minItems: 1 not met) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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