{"uid":"cap_ovdxhwcI98bNYg7i_xIuN","slug":"internal-support-string-normalisation-iqr-outlier-flags-7a791129","name":"Internal Support — String Normalisation + IQR Outlier Flags","description":"Internal support — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied internal support records (responded_requests, received_requests). 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-operations-22-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","responded_requests","received_requests"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"received_requests":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"responded_requests":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","received_requests":1,"responded_requests":10},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","received_requests":2,"responded_requests":20},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","received_requests":3,"responded_requests":15},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","received_requests":4,"responded_requests":30}],"maxItems":200,"minItems":1},"field":{"enum":["responded_requests","received_requests"],"type":"string","default":"responded_requests"}}},"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_7SnBXAfagTYkb5ORrr_LM","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 strings and flags IQR-based outliers in internal support records (responded_requests, received_requests) for up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and IQR outlier flagging on my support records? I have up to 50 rows with team_group, date, responded_requests, and received_requests — I want reports on responded_requests.","exampleUseCases":[{"title":"Outlier detection in daily support volume","prompt":"I have 30 days of support data across two teams with responded_requests and received_requests per day — can you normalize the strings and flag any IQR outliers in responded_requests so I can spot which days had abnormal activity?"},{"title":"Support team data quality audit","prompt":"My internal support CSV has team_group names that might be inconsistently formatted and some weird spikes in received_requests — can you normalize the strings and run IQR outlier detection on received_requests across all 120 rows?"},{"title":"Multi-team response rate anomaly report","prompt":"We track responded_requests and received_requests by team every day. Can you take these 80 rows, clean up the string fields, and produce both the normalisation and IQR outlier flag reports for responded_requests?"}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report standardising text fields such as team_group across the supplied rows, and (2) an IQR outlier flag report identifying statistical anomalies in the selected numeric field (responded_requests or received_requests) using interquartile range methodology. Results cover up to 200 input rows with no external lookups or predictions.","failureModes":["Input exceeds 200 rows — request rejected with validation error","Missing required fields (row_id, date, team_group, responded_requests, received_requests) cause row-level errors","Invalid date format (not YYYY-MM-DD) causes parsing failure","Numeric values outside allowed range trigger validation errors","Field selector not matching enum values ('responded_requests' or 'received_requests') causes schema rejection","Insufficient data points for meaningful IQR computation may produce degenerate outlier flags"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis on internal support operations data — specifically string normalisation combined with IQR-based outlier detection — without any external lookups, ML predictions, or live data sources. Ideal for batch-processing up to 200 rows of team support metrics at minimal cost ($0.01 USDC for two reports). Not suitable for real-time monitoring, predictive analytics, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:22.058Z","isFirstParty":false,"canonicalSlug":"internal-support-string-normalisation-iqr-outlier-flags-7a791129"}