{"uid":"cap_KhHEqgZjwjnBRmQKCVjTU","slug":"schema-conformity-iqr-outlier-flags-missing-value-fill-82abf48a","name":"Schema Conformity IQR Outlier Flags + Missing-Value Fill","description":"Schema conformity — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied schema conformity records (valid_records, checked_records). 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-analytics-02-180?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","valid_records","checked_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"valid_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"checked_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","valid_records":10,"checked_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","valid_records":20,"checked_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","valid_records":15,"checked_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","valid_records":30,"checked_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["valid_records","checked_records"],"type":"string","default":"valid_records"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_vfQNhswaRmsotGmFO9phA","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":"Detects IQR-based outliers and fills explicit missing values in schema conformity records (valid_records, checked_records) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"I have a schema conformity dataset with up to 50 rows, each with a date, dataset_group, valid_records, and checked_records — can you flag IQR outliers and fill any missing values in the valid_records column using median imputation?","exampleUseCases":[{"title":"Data pipeline quality audit","prompt":"I've got 120 rows of daily schema conformity records across two dataset groups with dates, valid_records, and checked_records — can you run IQR outlier detection and fill any missing valid_records values using median so I can spot anomalies in my pipeline?"},{"title":"Missing value cleanup before reporting","prompt":"Before I generate my weekly data quality report, can you fill in any missing checked_records values using mean imputation and flag any IQR outliers in that column across my 80-row conformity dataset?"},{"title":"Validate schema check metrics","prompt":"I have a time-series of schema conformity checks from October 2026 with around 30 rows — please identify which rows have outlier valid_records counts by IQR method and replace any nulls with zero so I have a clean dataset to work with."}],"resultDescription":"Two analytical reports: (1) IQR outlier flags per row for the selected field, indicating which rows fall outside the interquartile range; (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Results are computed locally with no external lookups.","failureModes":["Fewer than 1 row provided — minItems validation error","More than 200 rows submitted — maxItems limit exceeded","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Field value not in enum ['valid_records','checked_records'] — enum validation error","Method value not in enum ['zero','mean','median'] — enum validation error","All values in selected field are null — IQR computation may be undefined","Numeric values outside ±1,000,000,000,000 range — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier flagging and missing-value imputation on schema conformity metrics (valid_records, checked_records) in a single call for up to 200 rows. It is ideal for lightweight, offline data quality checks where no external data sources, live predictions, or professional assessments are required. Prefer this over general-purpose analytics when the input schema exactly matches the conformity record structure and you want both reports for a flat $0.01 USDC fee.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:11.409Z","isFirstParty":false,"canonicalSlug":"schema-conformity-iqr-outlier-flags-missing-value-fill-82abf48a"}