# Data Reconciliation: Record Quality Audit + String Normalisation + Missing-Value Fill

> Data Reconciliation: Record Quality Audit + String Normalisation + Missing-Value Fill 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).

Audits record quality, normalises strings, and fills missing values for up to 200 source/target reconciliation rows, returning 3 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-208?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-record-quality-audit-string-normalisation-missing-b75c5c55
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_rX4hsYflyNTRPra204uEZ

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-record-quality-audit-string-normalisation-missing-b75c5c55 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and median missing-value fill on my reconciliation data — here are 5 rows with row IDs, dataset groups, dates, source_total, and target_total values — and give me all 3 reports?

## When to prefer this

Choose this endpoint when you need three combined data reconciliation reports (quality audit, string normalisation, and missing-value imputation) in a single $0.01 call on up to 200 rows of source/target data. Ideal for pre-load validation, finance reconciliation QA, or ETL pipeline hygiene checks where you want offline, local analytics without any external lookups or predictions.

## Known failure modes

- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) per row — schema validation failure
- Date field not in YYYY-MM-DD format — pattern validation error
- Numeric values outside [-1,000,000,000,000, 1,000,000,000,000] range — schema rejection
- Invalid imputation method or field enum value — schema error
- Empty rows array — minItems violation

## How this service works

Data reconciliation — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit flagging issues like nulls, malformed dates, or out-of-range values; (2) a string normalisation report standardising dataset_group and other string fields; (3) a missing-value fill report with imputed values for the chosen field (source_total or target_total) using zero, mean, or median strategy. All computed locally from the supplied rows — no external data sources.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-record-quality-audit-string-normalisation-missing-b75c5c55/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)