# Study Recruitment Data Quality Audit, String Normalisation & Numeric Row Ordering

> Study Recruitment Data Quality Audit, String Normalisation & Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).

Runs three local analytical reports on study recruitment records: a record quality audit, string normalisation, and numeric row ordering — all on supplied data, no external calls.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-09-196?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/study-recruitment-data-quality-audit-string-normalisation-numeric-row-1b880afa
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_DAvvyHSqAv24CFoYR8Gug

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 study-recruitment-data-quality-audit-string-normalisation-numeric-row-1b880afa -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and numeric row ordering on my study recruitment data? I have rows with fields like row_id, course_group, date, eligible_candidates, and enrolled_candidates — up to 50 rows — and I want the analysis focused on eligible_candidates.

## When to prefer this

Choose this endpoint when you need a combined, cost-efficient bundle of three data quality operations — audit, normalisation, and ordering — on study recruitment data in a single call for $0.01 USDC. It is best suited for offline/batch data hygiene workflows on supplied datasets (up to 200 rows) where no live data lookup, external enrichment, prediction, or professional assessment is required. Prefer alternatives if you need real-time candidate screening, predictive enrollment modelling, or data from sources other than study recruitment (e.g. savings ledger or wallet inventory, which have their own sibling endpoints).

## Known failure modes

- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, date, course_group, eligible_candidates, enrolled_candidates) causing schema validation failure
- Invalid date format (must match YYYY-MM-DD pattern)
- Invalid field enum value — must be 'eligible_candidates' or 'enrolled_candidates'
- Empty rows array submitted (minItems: 1 required)
- Numeric values outside allowed range (±1,000,000,000,000)

## How this service works

Study recruitment — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied study recruitment records (eligible_candidates, enrolled_candidates). 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 derived purely from the supplied rows: (1) a record quality audit flagging missing, null, or malformed values; (2) a string normalisation report cleaning and standardising the course_group and other text fields; (3) a numeric row ordering report sorting rows by the selected numeric field (eligible_candidates or enrolled_candidates). Up to 200 rows supported per call. No external lookups or predictions are made.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/study-recruitment-data-quality-audit-string-normalisation-numeric-row-1b880afa/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)