# Study Recruitment String Normalisation & Adjacent Row Changes

> Study Recruitment String Normalisation & Adjacent Row Changes 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 two analytical reports on study recruitment data: string normalisation and adjacent row changes for eligible_candidates or enrolled_candidates across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-09-029?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-string-normalisation-adjacent-row-changes-4408279a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HcC2g8sylDGMYgH5Bf7Pv

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-string-normalisation-adjacent-row-changes-4408279a -d '<json body>'
```

Example prompt: Can you run the string normalisation and adjacent row changes reports on my study recruitment data? I have 4 rows with course groups group-a and group-b, dates from 2026-10-01 to 2026-10-04, and I want to analyze the eligible_candidates field.

## When to prefer this

Choose this endpoint when you have study recruitment tabular data (up to 200 rows) and need both string normalisation and adjacent-row-change analytics in a single low-cost call. It is purpose-built for course/group-level recruitment records with eligible and enrolled candidate counts, and bundles two reports for $0.01 USDC. Prefer it over general-purpose analytics tools when your data conforms to the row_id/course_group/date/eligible_candidates/enrolled_candidates schema and you want offline, supply-only analysis with no external lookups or predictions.

## Known failure modes

- Rows array empty or missing — validation error returned
- More than 200 rows submitted — request rejected with max-items error
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Missing required fields (row_id, course_group, date) — schema validation error
- Null numeric values may affect delta calculations in adjacent row changes report
- Invalid field enum value — rejected with enum validation error

## How this service works

Study recruitment — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied study recruitment records (eligible_candidates, enrolled_candidates). 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 independent analytical reports per call: (1) string normalisation output flagging or standardising text values in the dataset, and (2) adjacent row changes showing numeric deltas between consecutive rows for the selected metric (eligible_candidates or enrolled_candidates). Covers up to 200 input rows per call.

## 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-string-normalisation-adjacent-row-changes-4408279a/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)