{"uid":"cap_D92zvDtGnj-pOYyMjF9eI","slug":"business-event-participation-string-normalisation-adjacent-row-changes-7a494445","name":"Business Event Participation — String Normalisation + Adjacent Row Changes","description":"Business event participation — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes for supplied business event participation records (attendee_count, company_count). 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-company-19-029?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","attendee_count","company_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"company_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"attendee_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_count":1,"company_group":"group-a","attendee_count":10},{"date":"2026-10-02","row_id":"row-2","company_count":2,"company_group":"group-b","attendee_count":20},{"date":"2026-10-03","row_id":"row-3","company_count":3,"company_group":"group-a","attendee_count":15},{"date":"2026-10-04","row_id":"row-4","company_count":4,"company_group":"group-b","attendee_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["attendee_count","company_count"],"type":"string","default":"attendee_count"}}},"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_s5R9KLjuA_YGGHYFGjsQJ","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 company_group strings and detects adjacent row changes in attendee_count or company_count for business event participation data (up to 200 rows, 2 reports per call).","exampleAgentPrompt":"Can you run string normalisation on the company group names and detect adjacent row changes in attendee_count for my event data? I have 4 rows: row-1 on 2026-10-01, group-a, 10 attendees, 1 company; row-2 on 2026-10-02, group-b, 20 attendees, 2 companies; row-3 on 2026-10-03, group-a, 15 attendees, 3 companies; row-4 on 2026-10-04, group-b, 30 attendees, 4 companies.","exampleUseCases":[{"title":"Event series attendance shift detection","prompt":"I have weekly event participation records across two company groups over 4 dates — can you check attendee_count for adjacent row changes and also normalise the company_group strings? Rows: row-1, 2026-10-01, group-a, 10 attendees, 1 company; row-2, 2026-10-02, group-B, 20 attendees, 2 companies; row-3, 2026-10-03, Group-A, 15 attendees, 3 companies; row-4, 2026-10-04, group-b, 30 attendees, 4 companies."},{"title":"Company count sequential change analysis","prompt":"For my business event records, run the two reports on company_count — I want adjacent row changes detected and the company_group strings normalised. Here are the rows: row-1, 2026-11-01, alpha corp, 5 attendees, 3 companies; row-2, 2026-11-02, Beta Corp, 8 attendees, 7 companies; row-3, 2026-11-03, alpha corp, 6 attendees, 2 companies."},{"title":"Post-conference data cleanup and diff","prompt":"After our conference series, I need to clean up company group labels and spot where attendee_count jumped or dropped between consecutive days. Can you normalise the strings and flag adjacent row changes for these 3 rows: row-A, 2026-09-10, Innovate Hub, 50 attendees, 10 companies; row-B, 2026-09-11, innovate hub, 80 attendees, 12 companies; row-C, 2026-09-12, INNOVATE HUB, 45 attendees, 9 companies?"}],"resultDescription":"Returns two structured reports per call: (1) a string normalisation report for the company_group field across all supplied rows, and (2) an adjacent row changes report for the selected numeric field (attendee_count or company_count), covering sequential differences between consecutive rows — all computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, company_group, date, attendee_count, company_count) causes schema rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Submitting fewer than 1 row fails minItems constraint","Invalid field enum value (anything other than attendee_count or company_count) causes rejection"],"whenToPreferThis":"Choose this endpoint when you have tabular business event participation data (up to 200 rows) and need both string normalisation on company group labels AND sequential/adjacent row change detection on a numeric participation metric in a single low-cost call. It is ideal for post-event data cleaning pipelines, attendance trend QA, and lightweight analytical preprocessing where no external data lookup or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T12:44:27.459Z","isFirstParty":false,"canonicalSlug":"business-event-participation-string-normalisation-adjacent-row-changes-7a494445"}