# Schema Conformity Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series

> Schema Conformity Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series 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 analytics reports on schema conformity data — a record quality audit, string normalisation, and a trailing mean series — for up to 200 rows of valid/checked record counts.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-02-199?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/schema-conformity-analytics-record-quality-audit-string-normalisation-fea61bc6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FV7dD5k3mjWZT2pvqA6Vb

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 schema-conformity-analytics-record-quality-audit-string-normalisation-fea61bc6 -d '<json body>'
```

Example prompt: Run a record quality audit, string normalisation, and trailing mean series (window of 3) on my schema conformity data for the valid_records field — here are 5 rows: row-1 on 2026-10-01 group-a with 100 valid and 110 checked, row-2 on 2026-10-02 group-b with 95 valid and 105 checked, row-3 on 2026-10-03 group-a with 88 valid and 100 checked, row-4 on 2026-10-04 group-b with 120 valid and 130 checked, row-5 on 2026-10-05 group-a with 110 valid and 115 checked.

## When to prefer this

Choose this endpoint when you need three combined analytics reports — record quality audit, string normalisation, and trailing mean series — on schema conformity data in a single low-cost call. Ideal for data engineers auditing ETL pipeline quality, analysts computing rolling averages over validation metrics, or teams needing lightweight local analytics without external API dependencies. Best suited for datasets up to 200 rows where all three report types are needed together at minimal cost.

## Known failure modes

- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows supplied — exceeds maxItems limit
- Missing required fields (row_id, date, valid_records, checked_records) on any row
- Date field not matching YYYY-MM-DD pattern — validation error
- Window parameter outside 2–200 range
- Field enum value not one of 'valid_records' or 'checked_records'
- Payment not provided or insufficient — x402 payment required at $0.01 USDC

## How this service works

Schema conformity — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series 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

Three independent analytical reports: (1) a record quality audit summarising completeness and validity of the supplied rows, (2) a string normalisation report on text fields like dataset_group, and (3) a trailing mean series for the selected numeric field (valid_records or checked_records) computed over the specified window size. All reports are generated locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "valid_records",
     "checked_records"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "valid_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "checked_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "valid_records": 10,
     "checked_records": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "valid_records": 20,
     "checked_records": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "valid_records": 15,
     "checked_records": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "valid_records": 30,
     "checked_records": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "valid_records",
    "checked_records"
   ],
   "type": "string",
   "default": "valid_records"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/schema-conformity-analytics-record-quality-audit-string-normalisation-fea61bc6/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)