# Aggregation Checks — String Normalisation + Trailing Mean Series

> Aggregation Checks — String Normalisation + Trailing Mean Series 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).

Runs two local analytical reports on supplied aggregation check records: string normalisation and trailing mean series computation, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-17-028?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-trailing-mean-series-204f7a0d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_MGp-hdphYRYuF2xbnWsjX

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-trailing-mean-series-204f7a0d -d '<json body>'
```

Example prompt: Run the string normalisation and trailing mean series reports on my aggregation check records — here are 4 rows with source_sum and aggregate_sum values across groups A and B from October 2026, using field source_sum and a window of 3.

## When to prefer this

Choose this endpoint when you need two bundled local analytical operations — string normalisation and trailing mean series — on a small-to-medium set of aggregation check records (up to 200 rows) in a single low-cost call. Ideal for reconciliation workflows where you want to clean group labels and compute rolling averages on source or aggregate sums simultaneously without external API dependencies or live data lookups.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size larger than the number of rows — trailing mean series may be incomplete or invalid
- Missing required fields (row_id, date, dataset_group, source_sum, aggregate_sum) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside ±1,000,000,000,000 range — rejected
- Payment not attached or insufficient — x402 payment required error

## How this service works

Aggregation checks — String normalisation + Trailing mean series: String normalisation + Trailing mean series 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

Two independent analytical reports: (1) a string normalisation report on the dataset_group labels and row identifiers, and (2) a trailing mean series computed over the specified numeric field (source_sum or aggregate_sum) using the given rolling window size — all derived locally from the supplied rows 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/aggregation-checks-string-normalisation-trailing-mean-series-204f7a0d/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)