# Duplicate Observations Analytics — Record Quality Audit, String Normalisation & Keyed Dataset Comparison > Duplicate Observations Analytics — 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 2026-10-20). Runs three independent analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 duplicate-observation records containing duplicate_rows and total_rows fields. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-207?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-20 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/duplicate-observations-analytics-record-quality-audit-string-dd4005d5 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_7jenezgC6NEQCVya82n7Z 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 duplicate-observations-analytics-record-quality-audit-string-dd4005d5 -d '' ``` Example prompt: Can you run a full quality audit, string normalisation, and keyed comparison on my two duplicate-observation datasets? The first dataset has rows like row-1 on 2026-10-01 in group-a with 10 duplicate_rows out of 1 total_rows, and I want to compare it against a second dataset with slightly different duplicate counts for the same row IDs. ## When to prefer this Choose this endpoint when you need a bundled set of three analytical reports — quality audit, string normalisation, and keyed comparison — specifically for duplicate-observation records with duplicate_rows and total_rows fields, at low cost ($0.01 USDC for all three reports on up to 200 rows). It is ideal when you want local, offline analysis with no external data lookups, and when you have two parallel datasets to compare by key. Prefer alternatives if you need real-time deduplication, live database queries, or analysis of datasets larger than 200 rows. ## Known failure modes - Input array exceeds 200 rows — request rejected - Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — validation error - Invalid date format (must be YYYY-MM-DD) — schema validation failure - Numeric values outside allowed range (-1e12 to 1e12) — schema rejection - Empty rows array (minItems:1 not satisfied) — bad request - Mismatched row_id keys between rows and other arrays — comparison report may show unmatched records ## How this service works Duplicate observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied duplicate observations records (duplicate_rows, total_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 Returns three independent analytical reports: (1) a record quality audit flagging issues such as null values, out-of-range counts, or suspicious duplicate_rows/total_rows ratios; (2) a string normalisation report covering dataset_group and other string fields; and (3) a keyed dataset comparison report matching records from the 'rows' and 'other' input arrays by row_id to surface discrepancies in duplicate_rows and total_rows values. All analysis is performed locally with no external lookups. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "duplicate_rows", "total_rows" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "total_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 }, "duplicate_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "total_rows": 1, "dataset_group": "group-a", "duplicate_rows": 10 }, { "date": "2026-10-02", "row_id": "row-2", "total_rows": 2, "dataset_group": "group-b", "duplicate_rows": 20 }, { "date": "2026-10-03", "row_id": "row-3", "total_rows": 3, "dataset_group": "group-a", "duplicate_rows": 15 }, { "date": "2026-10-04", "row_id": "row-4", "total_rows": 4, "dataset_group": "group-b", "duplicate_rows": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "duplicate_rows", "total_rows" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "total_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 }, "duplicate_rows": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-analytics-record-quality-audit-string-dd4005d5/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)