# Training Compliance Data Auditor — Quality Audit, String Normalisation & Dataset Comparison > Training Compliance Data Auditor — Quality Audit, String Normalisation & 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 2026-10-26). Runs three independent analytical reports on training compliance records (completed_courses, assigned_courses): record quality audit, string normalisation, and keyed dataset comparison for up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-operations-10-207?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-26 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/training-compliance-data-auditor-quality-audit-string-normalisation-74602757 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_3XQgs0Gu5ElFsAPD4etPW 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 training-compliance-data-auditor-quality-audit-string-normalisation-74602757 -d '' ``` Example prompt: Can you run a full quality audit, string normalisation, and dataset comparison on my training compliance records? I have two sets of up to 50 rows each, with fields like row_id, team_group, date, completed_courses, and assigned_courses — I need all three reports back so I can spot data issues and differences between the two snapshots. ## When to prefer this Choose this endpoint when you need three bundled analytical reports on training compliance tabular data in a single low-cost call: quality auditing, string normalisation, and dataset diffing by key. It is ideal when you already have the data locally and need offline analysis without live lookups, external integrations, or ML predictions. Best suited for HR/L&D teams validating LMS exports before reporting or reconciling two compliance snapshots. ## Known failure modes - More than 200 rows submitted — request rejected with row count error - Missing required fields (row_id, team_group, date, completed_courses, assigned_courses) — validation error per row - Date field not matching YYYY-MM-DD pattern — pattern validation failure - Numeric fields outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — range error - Empty rows array (minItems:1 not met) — schema validation error - Payment not included or insufficient — x402 payment required response ## How this service works Training compliance — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied training compliance records (completed_courses, assigned_courses). 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: (1) a record quality audit highlighting nulls, out-of-range values, and malformed fields; (2) a string normalisation report with standardised team_group and other text fields; (3) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets matched by row_id. All processing is local with no external lookups. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "team_group", "date", "completed_courses", "assigned_courses" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "team_group": { "type": "string", "default": "", "maxLength": 80 }, "assigned_courses": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "completed_courses": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "team_group": "group-a", "assigned_courses": 1, "completed_courses": 10 }, { "date": "2026-10-02", "row_id": "row-2", "team_group": "group-b", "assigned_courses": 2, "completed_courses": 20 }, { "date": "2026-10-03", "row_id": "row-3", "team_group": "group-a", "assigned_courses": 3, "completed_courses": 15 }, { "date": "2026-10-04", "row_id": "row-4", "team_group": "group-b", "assigned_courses": 4, "completed_courses": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "team_group", "date", "completed_courses", "assigned_courses" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "team_group": { "type": "string", "default": "", "maxLength": 80 }, "assigned_courses": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "completed_courses": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additiona … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-compliance-data-auditor-quality-audit-string-normalisation-74602757/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)