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Form Inventories — Record Quality Audit + String Normalisation + Equal-Width Histogram
x402
Form Inventories — Record Quality Audit + String Normalisation + Equal-Width Histogram 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).
Runs three local analytical reports on up to 200 form inventory rows: a record quality audit, string normalisation, and an equal-width histogram over a chosen numeric field.
Price / call$0.01
Success—
StatusUnknown
How this service works
Form inventories — Record quality audit + String normalisation + Equal-width histogram: Record quality audit + String normalisation + Equal-width histogram for supplied form inventories records (field_count, labelled_fields). 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 missing, null, or out-of-range values per row; (2) a string normalisation report standardising text fields like site_group; and (3) an equal-width histogram breaking the chosen numeric field (field_count or labelled_fields) into the requested number of bins with counts per bin. All computed locally with no external lookups.
Example use cases
Form data quality check before reporting
“I've got 120 form inventory rows with site_group, date, field_count, and labelled_fields — can you audit their record quality and normalise the site_group strings, and also give me a 5-bin histogram over field_count so I can see the distribution before I write up my report?”
Detecting labelling gaps across site groups
“Run a quality audit and a 10-bin equal-width histogram on labelled_fields for my 80 form inventory records — I want to spot which bins have the fewest labelled fields and flag any rows with data quality problems.”
Cleaning and binning a new form inventory dataset
“I just collected a batch of 200 form inventory entries with field_count and labelled_fields columns — can you normalise the string fields, audit the record quality, and produce an equal-width histogram with 8 bins on labelled_fields?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-21-202?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations — quality audit, string normalisation, and histogram — on form inventory data in a single low-cost call. It is best suited for offline or batch analysis of up to 200 rows where no live data lookup, prediction, or external action is needed. Prefer it over separate single-report endpoints when all three analyses are required simultaneously to save cost and round-trips.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- bins value outside 1–30 range triggers schema validation failure
- Missing required fields (row_id, field_count, labelled_fields) cause record-level quality failures
- Entirely null numeric columns may yield degenerate histogram output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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