# Data Ingestion Batches — Record Quality Audit + String Normalisation + Keyed Dataset Comparison > Data Ingestion Batches — Record Quality Audit + String Normalisation + Keyed Dataset Comparison 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 analytical reports on supplied data ingestion batch records: a record quality audit, string normalisation, and keyed comparison between two datasets — all locally, with no external calls. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-207?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-17 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-9ddfa0c1 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_PU_MBi_D2KdgEOaBk_9-U Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently. ## How to call it through Zero Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`): ```sh zero fetch --capability data-ingestion-batches-record-quality-audit-string-normalisation-9ddfa0c1 -d '' ``` Example prompt: Can you run all three analytics reports — record quality audit, string normalisation, and keyed dataset comparison — on my two ingestion batch datasets? The primary dataset has rows like row-1 through row-4 for groups 'group-a' and 'group-b' from 2026-10-01 to 2026-10-04 with received_rows of 10, 20, 15, 30 and accepted_rows of 1, 2, 3, 4, and the comparison dataset has the same keys but received_rows of 10, 21, 17, 30. ## When to prefer this Choose this endpoint when you need three simultaneous analytical perspectives on data ingestion batch records — quality auditing, string normalisation, and cross-dataset comparison — in a single $0.01 call with up to 200 rows. Ideal for data engineers validating ETL pipeline outputs, comparing ingestion snapshots, or cleaning up group label strings, without requiring any external data lookups or live connections. ## Known failure modes - More than 200 rows submitted — request rejected due to row limit - Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error - Invalid date format (not YYYY-MM-DD) — schema validation failure - Numeric values out of allowed range (±1,000,000,000,000) — validation error - Empty rows array — rejected due to minItems:1 constraint - Payment not included or insufficient — x402 payment required error ## How this service works Data ingestion batches — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied data ingestion batches records (received_rows, accepted_rows). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows. ## Output Three independent analytical reports: (1) a record quality audit flagging completeness, validity, and anomalies in received_rows and accepted_rows; (2) a string normalisation report cleaning and standardising text fields like dataset_group; and (3) a keyed comparison report showing differences between the primary and secondary datasets matched by row_id. All reports are computed locally from supplied data only. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "received_rows", "accepted_rows" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "accepted_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 }, "received_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "accepted_rows": 1, "dataset_group": "group-a", "received_rows": 10 }, { "date": "2026-10-02", "row_id": "row-2", "accepted_rows": 2, "dataset_group": "group-b", "received_rows": 20 }, { "date": "2026-10-03", "row_id": "row-3", "accepted_rows": 3, "dataset_group": "group-a", "received_rows": 15 }, { "date": "2026-10-04", "row_id": "row-4", "accepted_rows": 4, "dataset_group": "group-b", "received_rows": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "received_rows", "accepted_rows" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "accepted_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 }, "received_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "de … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-ingestion-batches-record-quality-audit-string-normalisation-9ddfa0c1/health.json - [Zero catalog index](https://www.zero.xyz/llms.txt) - [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)