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Schema Conformity Analytics — Record Quality Audit, String Normalisation & Category Frequencies
x402
Schema Conformity Analytics — Record Quality Audit, String Normalisation & Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Runs three bundled analytical reports (record quality audit, string normalisation, and category frequency breakdown) on up to 200 schema conformity rows containing valid_records and checked_records fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Schema conformity — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied schema conformity records (valid_records, checked_records). 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 supplied schema conformity rows: (1) a record quality audit summarizing valid vs checked record ratios and flagging anomalies, (2) a string normalisation report cleaning and standardising text fields like dataset_group and row_id, and (3) a category frequency table showing how often each dataset_group appears across the supplied rows. All computed locally with no external lookups.
Example use cases
Data pipeline conformity health check
“I have a week's worth of schema conformity logs across two dataset groups — can you audit the record quality, normalize the string fields, and show me the category frequencies? Here are the rows: row-1 group-alpha 2026-11-01 valid=500 checked=520, row-2 group-beta 2026-11-02 valid=310 checked=400, row-3 group-alpha 2026-11-03 valid=480 checked=490.”
ETL validation summary by team
“Our ETL pipeline produces daily valid/checked record counts by team. Run the three conformity reports — quality audit, string normalisation, and category frequencies — on these 5 rows: row-1 team-ingest 2026-09-10 valid=1000 checked=1020, row-2 team-transform 2026-09-10 valid=850 checked=900, row-3 team-load 2026-09-10 valid=990 checked=995, row-4 team-ingest 2026-09-11 valid=1100 checked=1150, row-5 team-transform 2026-09-11 valid=800 checked=820.”
Monthly dataset group frequency breakdown
“I want to understand how my dataset groups break down over the month. Please run the category frequency analysis, string normalisation, and record quality audit on these conformity records: row-1 group-north 2026-10-01 valid=200 checked=210, row-2 group-south 2026-10-01 valid=180 checked=195, row-3 group-east 2026-10-02 valid=220 checked=225, row-4 group-west 2026-10-02 valid=170 checked=200.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-02-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical reports — quality audit, string normalisation, and category frequency — on schema conformity data in a single low-cost call ($0.01 USDC). It is best suited for datasets up to 200 rows where you want offline, deterministic analysis with no external API calls or predictions. Prefer alternatives if you need live data lookups, ML-based predictions, or processing more than 200 rows at once.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, valid_records, checked_records) causes row-level rejection
- Dates not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Payment failure (insufficient USDC balance) blocks the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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