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Content Length Observations — Record Quality Audit + String Normalisation + Daily Sum Series
x402
Content Length Observations — Record Quality Audit + String Normalisation + Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Runs three local analytical reports (record quality audit, string normalisation, and daily sum series) on up to 200 rows of content-length observation data (word_count, heading_count).
Price / call$0.01
Success—
StatusUnknown
How this service works
Content length observations — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum series for supplied content length observations records (word_count, heading_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 anomalies or inconsistencies in the supplied rows, (2) a string normalisation report cleaning and standardising string fields, and (3) a daily sum series aggregating the selected numeric field (word_count or heading_count) by date — all computed locally without external lookups.
Example use cases
Content quality audit for editorial team
“I've got 150 rows of article metrics with word_count and heading_count per site group and date — can you run the record quality audit, string normalisation, and daily sum series on them for word_count so I can spot any bad records before we report?”
Daily heading count aggregation for SEO dashboard
“Please run the three analytical reports — quality audit, string normalisation, and daily sum series — on my heading_count observations across these site groups and dates; I want to see how heading counts sum up per day.”
Data cleaning before content performance analysis
“Before I hand this content length dataset off to the analytics team, can you audit the record quality, normalise the string fields, and compute a daily sum series for word_count across all 80 rows I'm supplying?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-19-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, low-cost ($0.01 USDC) triple-report analysis — quality audit, string normalisation, and daily sum aggregation — on up to 200 rows of content-length observation data without requiring any live data lookups or external integrations. It is ideal for pre-processing content metrics datasets before downstream reporting or dashboard ingestion.
Side effects
- Submitting more than 200 rows results in rejection or truncation
- Missing required fields (row_id, site_group, date, word_count, heading_count) causes validation failure
- Malformed date strings not matching YYYY-MM-DD pattern will be rejected
- Null numeric values may be excluded from daily sum calculations
- Selecting an invalid field value outside the enum [word_count, heading_count] causes an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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