{"uid":"cap_3TRgKKRjY3f-iiBFuVTWp","slug":"local-event-calendars-string-normalisation-iqr-outlier-flags-6cb8d2bb","name":"Local Event Calendars — String Normalisation + IQR Outlier Flags","description":"Local event calendars — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied local event calendars records (listed_events, updated_events). 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-19-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","listed_events","updated_events"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"channel_group":{"type":"string","default":"","maxLength":80},"listed_events":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"updated_events":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","channel_group":"group-a","listed_events":10,"updated_events":1},{"date":"2026-10-02","row_id":"row-2","channel_group":"group-b","listed_events":20,"updated_events":2},{"date":"2026-10-03","row_id":"row-3","channel_group":"group-a","listed_events":15,"updated_events":3},{"date":"2026-10-04","row_id":"row-4","channel_group":"group-b","listed_events":30,"updated_events":4}],"maxItems":200,"minItems":1},"field":{"enum":["listed_events","updated_events"],"type":"string","default":"listed_events"}}},"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_vCCVv6z8nmFuTMaR4d_4J","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 flags IQR-based outliers in local event calendar records (listed_events, updated_events) for up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you normalise the string fields and flag any IQR outliers in my local event calendar data? I have rows with listed_events and updated_events counts across different channel groups — here are up to 200 rows, and I want reports on the listed_events field.","exampleUseCases":[{"title":"Audit weekly event listing anomalies","prompt":"I have a dataset of daily local event listings across two channel groups for the past month — can you run IQR outlier detection on the listed_events field and normalise the string columns so I can spot which dates had unusually high or low listing counts?"},{"title":"Clean event calendar data before dashboard load","prompt":"Before I load this into our dashboard, can you normalise the string fields and flag any outliers in both listed_events and updated_events for these 150 event calendar rows grouped by channel?"},{"title":"Detect unusual update activity across event channels","prompt":"I want to check if any channel groups are showing unusual updated_events counts compared to the rest — can you apply IQR outlier flagging to my updated_events data across these rows and also normalise the string fields?"}],"resultDescription":"Two independent analytical reports per call: one covering string normalisation of the submitted event calendar rows (standardising channel_group, date, and string fields), and one flagging rows whose listed_events or updated_events values fall outside IQR-derived bounds, indicating statistical outliers. Results cover up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, listed_events, updated_events) cause row-level errors","Date fields not matching YYYY-MM-DD pattern are rejected","Null or out-of-range numeric values may reduce outlier detection accuracy","Invalid field enum value (not 'listed_events' or 'updated_events') causes report failure"],"whenToPreferThis":"Choose this endpoint when you have local event calendar tabular data (up to 200 rows) and need both string normalisation and IQR-based outlier detection in a single low-cost call. It is ideal for pre-processing event data pipelines, auditing channel group reporting, or cleaning datasets before visualisation. It does not perform live lookups, external data enrichment, or predictions — use it purely for offline analytical cleaning of supplied records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T00:39:31.334Z","isFirstParty":false,"canonicalSlug":"local-event-calendars-string-normalisation-iqr-outlier-flags-6cb8d2bb"}