# Table Extraction — String Normalisation + Trailing Mean Series

> Table Extraction — 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 2026-10-23).

Analyzes table extraction records by applying string normalization and computing trailing mean series across up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-web-10-028?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/table-extraction-string-normalisation-trailing-mean-series-4d1196a7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JCh9y-AVRcfGIWAP2BGPy

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 table-extraction-string-normalisation-trailing-mean-series-4d1196a7 -d '<json body>'
```

Example prompt: Can you run string normalization and a trailing mean series analysis on my table extraction data? I have 4 rows for site groups 'group-a' and 'group-b' from October 2026 — use the extracted_rows field with a window of 2.

## When to prefer this

Choose this endpoint when you need both string normalization and rolling/trailing mean analysis on table extraction records in a single low-cost call ($0.01 USDC for both reports). Ideal for web scraping QA workflows, ETL auditing, or extraction accuracy monitoring where you have up to 200 date-stamped rows with extracted and expected row counts across site groups. Prefer this over general analytics tools when you want two bundled reports computed locally from your own supplied data without any external data fetching or live prediction.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Invalid date format (must match YYYY-MM-DD pattern)
- Missing required fields (row_id, site_group, date, extracted_rows, expected_rows)
- Window size out of range (must be 2–200)
- All values null in selected field — trailing mean cannot be computed
- Malformed JSON body returns validation error

## How this service works

Table extraction — String normalisation + Trailing mean series: String normalisation + Trailing mean series for supplied table extraction records (extracted_rows, expected_rows). 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 analytical reports for up to 200 input rows: (1) a string normalization report standardizing categorical/text fields in the extraction records, and (2) a trailing mean series computed over the specified numeric field (extracted_rows or expected_rows) using the given rolling window — all derived locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/table-extraction-string-normalisation-trailing-mean-series-4d1196a7/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)