{"uid":"cap_6t5EZVbOOMoYHXpVb4ISk","slug":"business-event-participation-adjacent-row-changes-iqr-outlier-flags-03b2b03b","name":"Business Event Participation — Adjacent Row Changes + IQR Outlier Flags","description":"Business event participation — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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-19-157?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_cMcsFczVu-brpbH4LraGa","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":"Computes adjacent-row delta changes and IQR-based outlier flags for business event participation data (attendee_count, company_count) across up to 200 rows.","exampleAgentPrompt":"Run adjacent row change analysis and IQR outlier flagging on my business event participation records — here are 4 rows: row-1 on 2026-10-01 with attendee_count 10 and company_count 1 in group-a, row-2 on 2026-10-02 with 20 attendees and 2 companies in group-b, row-3 on 2026-10-03 with 15 attendees and 3 companies in group-a, row-4 on 2026-10-04 with 30 attendees and 4 companies in group-b — analyze the attendee_count field.","exampleUseCases":[{"title":"Conference attendance anomaly detection","prompt":"I have 50 rows of conference event data with dates, attendee counts, and company counts by group — can you run adjacent row change detection and IQR outlier flagging on the attendee_count field so I can spot any unusually high or low attendance days?"},{"title":"Corporate event company participation audit","prompt":"Here are 120 rows of our corporate event participation records including row IDs, dates, company groups, and company counts — please analyze the company_count field for adjacent row changes and flag any IQR outliers so we can identify unusual participation spikes."},{"title":"Trade show series quality check","prompt":"I need to quality-check our trade show series data — I've got 80 event rows with attendee_count and company_count values across several company groups. Can you compute the row-over-row changes and flag outliers using IQR analysis on the attendee_count field?"}],"resultDescription":"Returns two analytical reports for the supplied rows: (1) adjacent row change values showing delta between consecutive rows for the chosen metric, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. Both reports are computed locally with no external data lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, company_group) — validation error","Date not in YYYY-MM-DD format — pattern validation failure","Invalid field selector (not attendee_count or company_count) — enum error","Empty rows array — minItems violation"],"whenToPreferThis":"Choose this endpoint when you have tabular business event participation data and need fast, local statistical analysis — specifically adjacent-row deltas and IQR-based anomaly detection — without any external lookup or live data enrichment. Ideal for data quality checks on event datasets up to 200 rows at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T18:57:17.857Z","isFirstParty":false,"canonicalSlug":"business-event-participation-adjacent-row-changes-iqr-outlier-flags-03b2b03b"}