# Humidity Observations — String Normalisation & Date by Category Pivot

> Humidity Observations — String Normalisation & Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Performs string normalisation and date-by-category pivot analysis on up to 200 rows of humidity observation data, returning 2 analytical reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-04-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/humidity-observations-string-normalisation-date-by-category-pivot-5d3c2ed4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-hsJ8c5xAyVgZUNA0PFbq

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 humidity-observations-string-normalisation-date-by-category-pivot-5d3c2ed4 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on my humidity observation records? I have 4 rows: station group-a on 2026-10-01 with humidity_percent 10 and observation_count 1, group-b on 2026-10-02 with humidity_percent 20 and observation_count 2, group-a on 2026-10-03 with 15% humidity and 3 observations, and group-b on 2026-10-04 with 30% humidity and 4 observations — analyse the humidity_percent field.

## When to prefer this

Choose this endpoint when you have up to 200 rows of humidity observation records and need both string normalisation of categorical fields (like station groups) and a date-by-category pivot of a numeric measure in a single low-cost call. It is ideal for lightweight, self-contained analytical preprocessing of weather station data without requiring an external database, live lookup, or prediction engine. At $0.01 USDC for two reports, it is cost-effective for small-batch data quality and summarisation tasks on structured humidity datasets.

## Known failure modes

- Exceeds 200 row limit — request rejected
- Missing required fields (row_id, station_group, date, humidity_percent, observation_count) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Invalid field enum value (must be 'humidity_percent' or 'observation_count') — rejected
- Empty rows array — schema minItems violation
- Payment not received or insufficient USDC — 402 response

## How this service works

Humidity observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied humidity observations records (humidity_percent, observation_count). 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 2 analytical reports: (1) a string normalisation report cleaning and standardising the station_group and other string fields in the supplied rows, and (2) a date-by-category pivot report cross-tabulating the selected numeric field (humidity_percent or observation_count) by date and station group category. Both reports are computed locally on the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/humidity-observations-string-normalisation-date-by-category-pivot-5d3c2ed4/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)