# Survey Participation Data Quality & Analytics — Audit, Normalisation & Trailing Mean

> Survey Participation Data Quality & Analytics — Audit, Normalisation & Trailing Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Runs three local analytical reports on survey participation data: record quality audit, string normalisation, and trailing mean series for respondents or invitations across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-10-199?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/survey-participation-data-quality-analytics-audit-normalisation-637655d6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TDpB8YUZavebDK224mxma

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 survey-participation-data-quality-analytics-audit-normalisation-637655d6 -d '<json body>'
```

Example prompt: Can you audit the quality of my survey participation records, normalise the course_group strings, and compute a 4-row trailing mean on the respondents field? I have about 80 rows covering groups like 'group-a' and 'group-b' with dates from 2026-10-01 onward.

## When to prefer this

Choose this endpoint when you need all three of record quality audit, string normalisation, and trailing mean series applied to survey participation data (respondents and invitations) in a single low-cost call. It is ideal for pre-analysis data cleaning workflows on education or course-group survey datasets of up to 200 rows where no external data enrichment or live prediction is needed. At $0.01 per call covering three reports, it is cost-efficient compared to calling separate single-report endpoints.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — rejected with validation error
- Missing required fields (row_id, course_group, date, respondents, invitations) on any row
- Date strings not matching YYYY-MM-DD pattern — schema validation failure
- Window size less than 2 or greater than 200 — rejected
- Invalid field enum value (must be 'respondents' or 'invitations')
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Payment not attached or insufficient USDC amount — 402 response

## How this service works

Survey participation — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied survey participation records (respondents, invitations). 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

Returns three independent analytical reports generated locally: (1) a record quality audit flagging issues in the supplied rows, (2) normalised string values for the course_group and other string fields, and (3) a trailing mean series for the selected numeric field (respondents or invitations) computed over the specified window size. All processing is performed on the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/survey-participation-data-quality-analytics-audit-normalisation-637655d6/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)