# Label Review Analytics: String Normalisation + Exact Row Deduplication

> Label Review Analytics: String Normalisation + Exact Row 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).

Runs two local analytical reports on label review data: string normalisation and exact row deduplication, for up to 200 rows of reviewed_records and changed_labels data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-020?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/label-review-analytics-string-normalisation-exact-row-deduplication-0a5c7881
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_jxNN2IE-tFbpvlwretJyN

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 label-review-analytics-string-normalisation-exact-row-deduplication-0a5c7881 -d '<json body>'
```

Example prompt: Can you run string normalisation and exact row deduplication on my label review data? I have rows with row_id, dataset_group, date, reviewed_records, and changed_labels fields — here are up to 200 rows I need both reports for.

## When to prefer this

Choose this endpoint when you need lightweight, offline data quality analysis specifically for label review records — no external API, no ML prediction, just fast local string normalisation and exact deduplication for batches up to 200 rows. Ideal for annotation pipeline auditing, label tracking cleanup, or pre-processing before model training data ingestion. Prefer this over full data warehouse deduplication tools when you have a small, well-structured label review batch and want two targeted reports at minimal cost.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values for reviewed_records or changed_labels outside allowed range cause rejection
- Empty rows array returns an error as minItems is 1

## How this service works

Label review — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied label review records (reviewed_records, changed_labels). 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 analytical reports per call: (1) a string normalisation report identifying and standardising inconsistent string values in the label review fields, and (2) an exact row deduplication report identifying and flagging fully duplicate rows across the supplied data. No external lookups or predictions are made; all analysis is performed locally on the supplied rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "reviewed_records",
     "changed_labels"
    ],
    "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
     },
     "changed_labels": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "reviewed_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "changed_labels": 1,
     "reviewed_records": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "changed_labels": 2,
     "reviewed_records": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "changed_labels": 3,
     "reviewed_records": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "changed_labels": 4,
     "reviewed_records": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/label-review-analytics-string-normalisation-exact-row-deduplication-0a5c7881/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)