# Sampling Observations Data Quality Suite: Record Audit + String Normalisation + Exact Deduplication

> Sampling Observations Data Quality Suite: Record Audit + String Normalisation + Exact Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Performs three combined data-quality operations on up to 200 sampling observation rows: a record quality audit, string field normalisation, and exact row deduplication — all processed locally without external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-191?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-data-quality-suite-record-audit-string-f7e27fff
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_b8pha7zoEVXNBPviDinM8

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-data-quality-suite-record-audit-string-f7e27fff -d '<json body>'
```

Example prompt: Can you run a full data quality check on my sampling observations — I need a record quality audit, string normalisation, and exact row deduplication on these rows: row-1 (2026-10-01, group-a, value=10, weight=1), row-2 (2026-10-02, group-b, value=20, weight=2), row-3 (2026-10-01, group-a, value=10, weight=1) — I want to spot duplicates and any messy string or quality issues.

## When to prefer this

Choose this endpoint when you need a bundled, cost-effective data quality pass over sampling observation records (with sample_value and sample_weight fields) and want all three reports — quality audit, string normalisation, and exact deduplication — in a single call for up to 200 rows. It is ideal for pre-analysis data cleaning pipelines where no external data enrichment or live prediction is needed and the dataset fits within the 200-row limit.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Rows missing required fields (row_id, date, sample_value, sample_weight, dataset_group) are rejected
- Dates not matching YYYY-MM-DD pattern cause schema validation failure
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error

## How this service works

Sampling observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication for supplied sampling observations records (sample_value, sample_weight). 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 for the submitted sampling observation rows: (1) a record quality audit identifying missing values, out-of-range figures, or malformed fields; (2) a string normalisation report standardising text fields like dataset_group; and (3) an exact row deduplication report identifying identical records. All processing is local — no external lookups or predictions are included.

## 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-data-quality-suite-record-audit-string-f7e27fff/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)