# Aggregation Checks — String Normalisation + Date by Category Pivot

> Aggregation Checks — 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 2026-10-24).

Performs string normalisation and date-by-category pivot analysis on up to 200 aggregation check records comparing source_sum and aggregate_sum values, returning 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-17-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/aggregation-checks-string-normalisation-date-by-category-pivot-6c49d5f3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_xTdYNbZh65aMD7xFnRSnV

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 aggregation-checks-string-normalisation-date-by-category-pivot-6c49d5f3 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on these aggregation check records — I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and I want to analyze the source_sum field?

## When to prefer this

Choose this endpoint when you need a quick, low-cost ($0.01 USDC) dual-report analysis combining string normalisation with a date-by-category pivot on up to 200 aggregation check records — particularly for ETL reconciliation, data warehouse audits, or financial aggregation QA where no live external data lookup is needed and you want two complementary views in a single call.

## Known failure modes

- Rows array empty or missing — returns validation error
- More than 200 rows submitted — rejected with limit error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- source_sum or aggregate_sum out of range ±1,000,000,000,000 — rejected
- field enum value not 'source_sum' or 'aggregate_sum' — schema error
- row_id missing or exceeds 80 characters — validation failure

## How this service works

Aggregation checks — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied aggregation checks records (source_sum, aggregate_sum). 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 that standardises text fields across the supplied rows, and (2) a date-by-category pivot table that groups and summarises the selected numeric field (source_sum or aggregate_sum) by dataset_group and date — all 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",
     "dataset_group",
     "date",
     "source_sum",
     "aggregate_sum"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_sum": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "aggregate_sum": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_sum": 10,
     "aggregate_sum": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_sum": 20,
     "aggregate_sum": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_sum": 15,
     "aggregate_sum": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_sum": 30,
     "aggregate_sum": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_sum",
    "aggregate_sum"
   ],
   "type": "string",
   "default": "source_sum"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/aggregation-checks-string-normalisation-date-by-category-pivot-6c49d5f3/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)