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Product Photography Analytics — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Product Photography Analytics — Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Runs three local analytical reports on product photography records: record quality audit, string normalisation, and trailing mean series for required_images or completed_images fields — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Product photography — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied product photography records (required_images, completed_images). 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 for the submitted rows: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report standardising store_group and other text fields; and (3) a trailing mean series for the selected numeric field (required_images or completed_images) computed with the specified window size. All analysis is performed locally with no external lookups.
Example use cases
Photography completion data quality check
“I have 50 rows of product photography records for our retail stores — can you run a record quality audit and string normalisation on them, and also produce a trailing mean of completed_images with a window of 4? The rows span October 2026 across three store groups.”
Rolling average for photography planning
“Take these 80 product photography tracking records and calculate a trailing mean series for required_images with a window of 5, plus flag any data quality issues and normalise the store_group strings while you're at it.”
Retail ops audit across store groups
“We've got 120 rows of required_images and completed_images data across our store groups and I want to audit them for quality, normalise any inconsistent store group labels, and get a smoothed 7-row rolling average of required_images — can you run all three reports at once?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-09-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and trailing mean series — on product photography records in a single low-cost call. Ideal for retail operations teams validating photography completion data across store groups without needing live external data, predictions, or professional assessments. Best suited for batch sizes up to 200 rows where cost efficiency matters ($0.01 for all three reports).
Side effects
- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) — row fails schema validation
- Window size outside 2–200 range — rejected with parameter error
- Missing required fields (row_id, date, required_images, completed_images) — row-level error
- Payment not included or insufficient — 402 Payment Required response
- Null numeric values may reduce trailing mean coverage for affected rows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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