{"uid":"cap_G8eJP-VZQCZs2sK7EpBZj","slug":"onboarding-progress-string-normalisation-iqr-outlier-flags-7b265c9f","name":"Onboarding Progress — String Normalisation + IQR Outlier Flags","description":"Onboarding progress — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied onboarding progress records (completed_steps, required_steps). 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-08-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","completed_steps","required_steps"],"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},"required_steps":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"completed_steps":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","required_steps":1,"completed_steps":10},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","required_steps":2,"completed_steps":20},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","required_steps":3,"completed_steps":15},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","required_steps":4,"completed_steps":30}],"maxItems":200,"minItems":1},"field":{"enum":["completed_steps","required_steps"],"type":"string","default":"completed_steps"}}},"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_DGalilL2e6Y70Z55pDe0X","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 onboarding progress records (completed_steps, required_steps) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"Can you normalise the string fields and flag IQR outliers in our onboarding progress data? I have 4 rows with team groups like group-a and group-b, dates from 2026-10-01 to 2026-10-04, and I want to analyse the completed_steps field.","exampleUseCases":[{"title":"Audit onboarding completion anomalies","prompt":"I have 150 rows of onboarding progress records with completed_steps and required_steps for various team groups — can you normalise the string columns and flag any IQR outliers in the completed_steps field so I can see which employees have unusually high or low completion counts?"},{"title":"Clean team group strings for reporting","prompt":"Our onboarding dataset for Q4 has inconsistent team group labels and messy step counts across about 80 rows — run string normalisation and IQR outlier detection on the required_steps field so I can clean this up before sending it to leadership."},{"title":"Detect data entry errors in step records","prompt":"I suspect some of our onboarding records have data entry mistakes in the completed_steps numbers — can you take these 50 rows with dates and team groups and run the IQR outlier flagging plus string normalisation to surface anything that looks statistically off?"}],"resultDescription":"Two analytical reports per call: one covering string normalisation results for the supplied onboarding rows, and one flagging rows whose completed_steps or required_steps values fall outside the interquartile range (IQR) bounds, indicating statistical outliers. Reports are based entirely on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, team_group, completed_steps, required_steps) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Field selector not one of 'completed_steps' or 'required_steps' — enum validation error","Too few rows to compute meaningful IQR (e.g. single row) — may return degenerate or empty outlier flags","Payment not provided or insufficient USDC — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have onboarding progress 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 lightweight data quality audits on completed_steps or required_steps without needing a full analytics platform. Prefer alternatives if you need predictive modelling, live data lookup, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-04T14:28:35.171Z","isFirstParty":false,"canonicalSlug":"onboarding-progress-string-normalisation-iqr-outlier-flags-7b265c9f"}