{"uid":"cap_FYyvVkDnkdRV7e75E49jf","slug":"interview-transcripts-string-normalisation-date-by-category-pivot-538af536","name":"Interview Transcripts — String Normalisation + Date by Category Pivot","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-news-12-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"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"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_sK9JRMJOCmazqMYzOPd02","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Normalises string fields and produces a date-by-category pivot table for up to 200 interview transcript records (transcript_words, audio_minutes).","exampleAgentPrompt":"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.","exampleUseCases":[{"title":"Research team transcript volume report","prompt":"I have 120 interview transcript records with channel groups like 'in-person' and 'remote', dates ranging across Q3, and word counts per session. Can you normalise the string fields and pivot the transcript_words by date and channel group so I can see volume trends?"},{"title":"Podcast channel audio minutes breakdown","prompt":"I've got 80 rows of podcast interview data with audio_minutes and channel_group fields. Please run the string normalisation and date-by-category pivot on the audio_minutes field so I can see how recording time is distributed across channels over time."},{"title":"HR interview data quality and summary","prompt":"We collected 40 HR interview records with transcript_words, audio_minutes, date, and department as the channel_group. Can you normalise the text fields and give me a pivot of transcript_words by date and department category?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:34:04.987Z","isFirstParty":false,"canonicalSlug":"interview-transcripts-string-normalisation-date-by-category-pivot-538af536"}