{"uid":"cap_9-zWo9yHE0aN1Yb6btfy6","slug":"business-event-participation-data-quality-distribution-analyzer-c45632a6","name":"Business Event Participation Data Quality & Distribution Analyzer","description":"Business event participation — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution for supplied business event participation records (attendee_count, company_count). 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-company-19-193?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_ReJvNq3Trn3gaGwho_Ghp","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":"Runs three local analytical reports on business event participation records: record quality audit, string normalization, and numeric distribution for attendee_count and company_count fields.","exampleAgentPrompt":"Can you run a full data quality audit, string normalization, and numeric distribution report on my business event participation records? I have rows with fields like row_id, company_group (e.g. 'group-a', 'group-b'), date (e.g. 2026-10-01), attendee_count, and company_count — up to 200 rows — and I want all three reports in one go, analyzing the attendee_count field.","exampleUseCases":[{"title":"Pre-analysis event data quality check","prompt":"I have 150 rows of business event participation data with attendee counts and company counts per event — can you audit the record quality, normalize the company_group strings, and show me the numeric distribution of attendee_count before I run my analysis?"},{"title":"Spotting anomalies in company attendance figures","prompt":"My event dataset has company_count values that look off — some might be nulls or outliers. Can you run a record quality audit and numeric distribution report on these company_count figures across my 80 event rows?"},{"title":"Cleaning inconsistent event group labels","prompt":"We collected business event participation data across multiple company groups but the naming is inconsistent. Can you normalize the company_group strings and audit record quality for these 60 rows so I can standardize before merging datasets?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing, malformed, or out-of-range values; (2) a string normalization report identifying and correcting inconsistencies in company_group labels; and (3) a numeric distribution report summarizing the statistical distribution (e.g. min, max, mean, histogram bins) of the selected numeric field (attendee_count or company_count) across all submitted rows. All computed locally with no external lookups.","failureModes":["Row count exceeds 200 items — request rejected","Missing required fields (row_id, company_group, date, attendee_count, company_count) cause validation errors","Invalid date format (not YYYY-MM-DD) causes row-level rejection","Numeric values outside allowed range (±1,000,000,000,000) may fail schema validation","Empty rows array (minItems: 1 not met) causes request failure","Payment not supplied or insufficient USDC causes x402 payment error"],"whenToPreferThis":"Choose this endpoint when you need a quick, affordable ($0.01) batch data profiling pass on up to 200 business event participation records and want three reports — quality audit, string normalization, and numeric distribution — in a single call without any external data lookups or AI inference. Ideal for pre-processing pipelines where you need to validate and profile raw event attendance data locally before downstream analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:30.795Z","isFirstParty":false,"canonicalSlug":"business-event-participation-data-quality-distribution-analyzer-c45632a6"}