# Interview Transcripts — String Normalisation + Date by Category Pivot

> Interview Transcripts — String Normalisation + Date by Category Pivot 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).

Normalises string fields and produces a date-by-category pivot table for up to 200 interview transcript records (transcript_words, audio_minutes).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-news-12-035?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/interview-transcripts-string-normalisation-date-by-category-pivot-538af536
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FYyvVkDnkdRV7e75E49jf

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 interview-transcripts-string-normalisation-date-by-category-pivot-538af536 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on my interview transcript records? I have up to 50 rows with fields row_id, channel_group, date, transcript_words, and audio_minutes — please analyse the transcript_words field.

## When to prefer this

Choose this endpoint when you have structured interview transcript data (up to 200 rows) and need both string normalisation and a date-by-category pivot in a single low-cost call. It is ideal for qualitative research workflows, podcast or interview analytics, and HR data summarisation where you want cleaned string fields and a grouped time-series view of word counts or audio durations without any live lookups or external model inference.

## Known failure modes

- More than 200 rows submitted — request rejected with row limit error
- Missing required fields (row_id, channel_group, date, transcript_words, audio_minutes) cause validation failure
- Date field not matching YYYY-MM-DD pattern causes schema validation error
- Invalid field selector (not 'transcript_words' or 'audio_minutes') causes enum validation error
- Empty rows array (minItems: 1 not met) causes validation error
- Numeric values outside allowed range trigger boundary errors

## How this service works

Interview transcripts — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied interview transcripts records (transcript_words, audio_minutes). 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

Returns two analytical reports: (1) a string normalisation report cleaning and standardising string fields in the supplied interview transcript rows, and (2) a date-by-category pivot table aggregating the selected numeric field (transcript_words or audio_minutes) broken down by date and channel_group, for up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/interview-transcripts-string-normalisation-date-by-category-pivot-538af536/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)