# Forecast Verification Inputs — Record Quality Audit + String Normalisation + Duplicate Key Report

> Forecast Verification Inputs — Record Quality Audit + String Normalisation + Duplicate Key Report is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).

Runs three local analytical reports (record quality audit, string normalisation, and duplicate key detection) on up to 200 rows of forecast-vs-observed weather verification data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-24-192?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/forecast-verification-inputs-record-quality-audit-string-3b2274db
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0HiVUzvSWpzIciaTpSqGz

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 forecast-verification-inputs-record-quality-audit-string-3b2274db -d '<json body>'
```

Example prompt: Can you run a full data quality check on my forecast verification records — I have about 50 rows with fields like row_id, station_group, date, forecast_value, and observed_value, and I need a record quality audit, string normalisation, and duplicate key report all in one go?

## When to prefer this

Choose this endpoint when you need a quick, cheap ($0.01 USDC) pre-processing quality check on forecast verification data before running statistical accuracy metrics. It is ideal when your dataset is under 200 rows and you need three reports (quality audit, string normalisation, duplicate detection) in a single call without any external data lookups or live weather data access. Prefer this over generic data validation tools when your data specifically has forecast_value and observed_value paired with station groups and dates.

## Known failure modes

- Empty rows array or missing required fields (row_id, date, forecast_value, observed_value) causes validation error
- More than 200 rows submitted exceeds the maxItems limit and will be rejected
- Date strings not matching YYYY-MM-DD pattern will fail schema validation
- Numeric values outside ±1,000,000,000,000 range will be rejected
- Malformed JSON body returns an error response

## How this service works

Forecast verification inputs — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied forecast verification inputs records (forecast_value, observed_value). 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

Three structured reports returned together: (1) a record quality audit flagging missing, null, or out-of-range values in each row; (2) a string normalisation report suggesting or applying consistent casing/formatting to station_group strings; (3) a duplicate key report identifying rows that share the same row_id or station_group/date combination. All analysis is performed locally with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/forecast-verification-inputs-record-quality-audit-string-3b2274db/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)