# Sampling Observations — Record Quality Audit + String Normalisation

> Sampling Observations — Record Quality Audit + String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Audits data quality and normalises strings in sampling observation records (sample_value, sample_weight) for up to 200 rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-001?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-record-quality-audit-string-normalisation-0318a06f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AW2GY7zHW8vCiJxg57Q6z

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 sampling-observations-record-quality-audit-string-normalisation-0318a06f -d '<json body>'
```

Example prompt: Can you run a record quality audit and string normalisation on my sampling observations? I have about 150 rows with row IDs, dataset groups, dates, sample values, and sample weights — I want two reports flagging any quality issues and normalising the strings.

## When to prefer this

Choose this endpoint when you need a fast, local, two-report quality assessment of sampling observation records specifically containing sample_value and sample_weight fields, for up to 200 rows, with no need for live lookups, predictions, or external data enrichment. Prefer it over sibling endpoints when you only need record quality audit and string normalisation (without grouped numeric summary, deduplication, or correlation analysis).

## Known failure modes

- More than 200 rows submitted — request rejected with maxItems validation error
- Missing required fields (row_id, date, sample_value, sample_weight, dataset_group) — row-level validation failure
- Date field not matching YYYY-MM-DD pattern — schema validation error
- Numeric values outside allowed range (-1e12 to 1e12) — validation rejection
- Empty rows array — minItems constraint failure
- Payment not completed via x402 — request blocked

## How this service works

Sampling observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied sampling observations records (sample_value, sample_weight). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Two independent reports: (1) a record quality audit identifying issues such as nulls, out-of-range values, or malformed rows across the supplied sampling observations; (2) a string normalisation report detailing inconsistencies, whitespace problems, or casing issues in string fields like dataset_group. Both 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",
     "sample_value",
     "sample_weight"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "sample_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "sample_weight": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "sample_value": 10,
     "dataset_group": "group-a",
     "sample_weight": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "sample_value": 20,
     "dataset_group": "group-b",
     "sample_weight": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "sample_value": 15,
     "dataset_group": "group-a",
     "sample_weight": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "sample_value": 30,
     "dataset_group": "group-b",
     "sample_weight": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-record-quality-audit-string-normalisation-0318a06f/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)