{"uid":"cap_6VpUU_i8DRAT0US1Aaym0","slug":"schema-conformity-numeric-interval-filter-iqr-outlier-flags-d9b25372","name":"Schema Conformity — Numeric Interval Filter + IQR Outlier Flags","description":"Schema conformity — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags 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-02-130?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_o1TWobuGZ6Y0bVZHsnTvU","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":"Filters schema conformity records by a numeric interval and flags IQR outliers for valid_records or checked_records fields across up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Check my schema conformity data for outliers — I have up to 50 rows with valid_records and checked_records fields per dataset group per day, and I want you to filter valid_records between 0 and 500 and flag any IQR outliers in that field.","exampleUseCases":[{"title":"Daily data pipeline quality audit","prompt":"I have 120 rows of schema conformity logs from my ETL pipeline, each with a date, dataset_group, valid_records, and checked_records. Filter valid_records between 10 and 5000 and flag any IQR outliers in valid_records so I can see which days had anomalous validation counts."},{"title":"Detecting bad batches in checked_records","prompt":"Run an IQR outlier analysis on my checked_records field across these 80 conformity rows — I want to filter to values between 0 and 1000 and see which dataset groups are flagging as statistical outliers."},{"title":"Schema validation monitoring across groups","prompt":"I've got conformity records for group-a and group-b across 30 dates. Can you filter valid_records between 5 and 200 and identify any rows whose valid_records count is an IQR outlier? I need both the interval-filtered list and the outlier flags."}],"resultDescription":"Returns two independent analytical reports per call: (1) a numeric interval filter report showing which rows fall within the specified min/max range for the chosen field, and (2) an IQR outlier flag report identifying rows whose field values are statistical outliers based on interquartile range analysis. Both reports operate on the same supplied rows (up to 200) without any external data lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row rejection","minimum greater than maximum produces empty interval filter results","All null values in the selected field may prevent IQR computation","Missing required fields (row_id, dataset_group, date) cause schema validation errors","Field enum value not in ['valid_records', 'checked_records'] causes rejection"],"whenToPreferThis":"Choose this endpoint when you need both range-based filtering and IQR-based outlier detection on schema conformity metrics (valid_records or checked_records) in a single low-cost call. It is ideal for data quality monitoring pipelines where you have up to 200 pre-aggregated conformity rows and want two complementary statistical views without any live external data access. Prefer alternatives if you need more than 200 rows, require real-time data fetching, or need outlier methods beyond IQR (e.g. z-score, DBSCAN).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T00:50:35.796Z","isFirstParty":false,"canonicalSlug":"schema-conformity-numeric-interval-filter-iqr-outlier-flags-d9b25372"}