# Structured Data Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes

> Structured Data Observations — Record Quality Audit + String Normalisation + Adjacent Row Changes 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 analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 rows of structured observations data containing valid_items and total_items fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-web-08-200?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/structured-data-observations-record-quality-audit-string-51064fe8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ci77TiMhZLPnZNGI9_apm

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 structured-data-observations-record-quality-audit-string-51064fe8 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and adjacent row changes analysis on my structured observations data? I have 12 rows with fields row_id, site_group, date, valid_items, and total_items — focus the field analysis on valid_items.

## When to prefer this

Choose this endpoint when you need three complementary data-quality reports (quality audit, string normalisation, and adjacent row changes) on structured observation records in a single call. It is ideal for datasets up to 200 rows where all analysis should be performed locally on supplied data without any external lookups, predictions, or live data access. It is especially useful when your records include both numeric metrics (valid_items, total_items) and categorical text fields (site_group) that may need cleaning.

## Known failure modes

- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, site_group, date, valid_items, total_items) in one or more row objects
- Date field does not match YYYY-MM-DD pattern — validation error
- Field selector not one of the allowed enum values (valid_items, total_items)
- Payment not included or insufficient — x402 payment required at $0.01 USDC

## How this service works

Structured data observations — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied structured data observations records (valid_items, total_items). 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 for the supplied rows: (1) a record quality audit flagging nulls, missing fields, and anomalies; (2) a string normalisation report identifying inconsistencies in text fields like site_group; and (3) an adjacent row changes report showing value differences between consecutive rows for the selected field (valid_items or total_items). All analysis is performed locally on the supplied data — no external lookups or predictions.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/structured-data-observations-record-quality-audit-string-51064fe8/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)