# Data Reconciliation — String Normalisation + Trailing Mean Series

> Data Reconciliation — 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 2026-10-19).

Runs two local analytical reports on supplied reconciliation data: string normalisation of row identifiers and a trailing mean series over source/target totals, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-028?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-string-normalisation-trailing-mean-series-a698e105
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QTwdxgUsHsRxyN7VM-Tjv

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 data-reconciliation-string-normalisation-trailing-mean-series-a698e105 -d '<json body>'
```

Example prompt: I have 50 reconciliation rows with source_total and target_total values across two dataset groups for October 2026 — can you normalise the row ID strings and compute a trailing mean over source_total with a window of 5?

## When to prefer this

Choose this endpoint when you need two bundled local analytical operations — string normalisation and trailing mean computation — applied to your own supplied reconciliation records without any external data fetching or live predictions. It is cost-effective at $0.01 USDC for both reports on up to 200 rows. Prefer it over general-purpose analytics when the input schema (source_total, target_total, dataset_group, date, row_id) matches your reconciliation data structure exactly.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, source_total, target_total) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Window size outside 2–200 range is rejected
- Non-numeric values in source_total or target_total fields cause parsing errors
- Fewer rows than the window size may produce incomplete trailing mean output

## How this service works

Data reconciliation — String normalisation + Trailing mean series: String normalisation + Trailing mean series for supplied data reconciliation records (source_total, target_total). 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 analytical reports: (1) string-normalised versions of the row identifier fields across the supplied records, and (2) a trailing mean series computed over the chosen field (source_total or target_total) using the specified window size, for up to 200 input rows. No external lookups or live data are used.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_total",
     "target_total"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "target_total": {
      "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_total": 10,
     "target_total": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_total": 20,
     "target_total": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_total": 15,
     "target_total": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_total": 30,
     "target_total": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-string-normalisation-trailing-mean-series-a698e105/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)