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Camera Coverage Record Quality Audit, String Normalisation & Duplicate Key Report
x402
Camera Coverage Record Quality Audit, String Normalisation & Duplicate Key Report is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Runs three local analytical reports on supplied camera coverage records (shot_count, coverage_seconds): a record quality audit, string normalisation, and duplicate key detection — for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Camera coverage — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied camera coverage records (shot_count, coverage_seconds). 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 reports for the supplied camera coverage rows: (1) a record quality audit highlighting missing, out-of-range, or malformed values; (2) a string normalisation report showing inconsistencies or suggested corrections in string fields like asset_group; and (3) a duplicate key report identifying rows sharing the same key identifiers such as row_id or asset_group/date combinations. All analysis is performed locally with no external lookups.
Example use cases
Pre-delivery camera log QA check
“Before I send this camera coverage log to the client, can you audit the record quality, normalise any inconsistent strings in asset_group, and flag any duplicate row_ids? I'll paste in the rows now — there are about 80 of them with shot_count and coverage_seconds values.”
Detecting duplicate shot records in production tracker
“We've been entering shot coverage data manually and I suspect there are duplicate entries by row_id or asset_group. Can you run a duplicate key report on these 150 camera coverage records and also check their overall data quality?”
Cleaning coverage data before analytics pipeline
“I need to normalise the string fields and audit the data quality of this batch of camera coverage records before we load them into our analytics pipeline. There are about 120 rows with date, asset_group, shot_count, and coverage_seconds — can you generate all three reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-12-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit record quality, normalise strings, and detect duplicate keys in camera coverage data (shot_count, coverage_seconds) in a single low-cost call. It is ideal for pre-pipeline data cleaning, client delivery QA, or validating manually entered camera logs. It operates entirely on supplied data with no external lookups, making it fast, deterministic, and suitable for sensitive or offline datasets of up to 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, asset_group, date, shot_count, coverage_seconds) causes row-level or request-level rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- shot_count or coverage_seconds outside ±1,000,000,000,000 bounds rejected
- Empty rows array (minItems:1 not met) returns error
- Malformed JSON body returns 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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