# Label Review Analytics — String Normalisation & Category Frequencies

> Label Review Analytics — String Normalisation & Category Frequencies 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 string normalisation and category frequency analysis on label review records (reviewed_records, changed_labels) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-024?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-category-frequencies-76a22be3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_VlViFkFUX3-YD-lPfb9oK

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-category-frequencies-76a22be3 -d '<json body>'
```

Example prompt: Can you run string normalisation and category frequency analysis on my label review data? I have up to 50 rows across two dataset groups — 'production' and 'staging' — each with a date, reviewed_records count, and changed_labels count, and I need both reports back.

## When to prefer this

Choose this endpoint when you need lightweight, offline analytical processing of label review records — specifically string normalisation and category frequency computation — on datasets of up to 200 rows, without requiring live data lookups, predictions, or external API calls. It is ideal for ML teams auditing annotation quality, data engineers normalising label pipeline outputs, or analysts summarising label change rates by dataset group. At $0.01 for two reports, it is cost-effective for small batch analytical tasks on structured label review data.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) rejected by schema
- Empty rows array (minItems:1 not met) causes request failure
- Payment of 0.01 USDC not fulfilled — endpoint returns 402 Payment Required

## How this service works

Label review — String normalisation + Category frequencies: String normalisation + Category frequencies 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

Returns two independent analytical reports: (1) a string normalisation report covering standardisation of string fields across the supplied rows, and (2) a category frequency report showing distribution and frequencies of reviewed_records and changed_labels broken down by dataset_group and date — all computed locally without any external lookup or prediction.

## 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-category-frequencies-76a22be3/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)