# Issue Throughput Data Quality Bundle: Audit + String Normalisation + Missing-Value Fill

> Issue Throughput Data Quality Bundle: 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 2026-10-25).

Runs three data-quality reports on issue throughput records (issues_opened, issues_closed): a record quality audit, string normalisation, and explicit missing-value fill — all processed locally with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-software-13-208?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/issue-throughput-data-quality-bundle-audit-string-normalisation-d7dc0c8c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6l7ZWu7qZ5DivTC5uRUGc

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 issue-throughput-data-quality-bundle-audit-string-normalisation-d7dc0c8c -d '<json body>'
```

Example prompt: Can you audit and clean this batch of issue throughput records — check record quality, normalise the component name strings, and fill any missing issues_opened values using the median? I have about 50 rows with fields: row_id, component, date, issues_opened, and issues_closed.

## When to prefer this

Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for three reports) data-quality pass on issue throughput records without requiring any live external data, predictions, or professional assessment. It is ideal for pre-processing software engineering metrics (issues opened/closed) before downstream analysis or dashboarding, especially when you need record auditing, string normalisation, and missing-value imputation in a single call for up to 200 rows.

## Known failure modes

- Exceeding 200-row limit returns a validation error
- Missing required fields (row_id, component, date, issues_opened, issues_closed) cause row-level errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Unsupported fill method enum value returns schema validation error
- Empty rows array rejected by minItems:1 constraint
- Payment failure via x402 prevents processing

## How this service works

Issue throughput — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied issue throughput records (issues_opened, issues_closed). 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 data-quality reports covering: (1) a record quality audit flagging malformed, inconsistent, or invalid rows; (2) normalised string values for text fields such as component names; and (3) explicit imputed values replacing nulls in the target numeric field (issues_opened or issues_closed) using the chosen fill method (zero, mean, or median). All reports are generated 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",
     "component",
     "date",
     "issues_opened",
     "issues_closed"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "component": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "issues_closed": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "issues_opened": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "component": "group-a",
     "issues_closed": 1,
     "issues_opened": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "component": "group-b",
     "issues_closed": 2,
     "issues_opened": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "component": "group-a",
     "issues_closed": 3,
     "issues_opened": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "component": "group-b",
     "issues_closed": 4,
     "issues_opened": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "issues_opened",
    "issues_closed"
   ],
   "type": "string",
   "default": "issues_opened"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/issue-throughput-data-quality-bundle-audit-string-normalisation-d7dc0c8c/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)