{"uid":"cap_-pdAzgIqI-DKGM_uhELrz","slug":"schema-conformity-numeric-interval-filter-adjacent-row-changes-4e94221c","name":"Schema Conformity — Numeric Interval Filter + Adjacent Row Changes","description":"Schema conformity — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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-02-127?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_P2N1rbM0wF3o8sPqejNKT","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 rows by a numeric interval on valid_records or checked_records, then reports adjacent row-to-row changes across up to 200 records in two independent analytical reports.","exampleAgentPrompt":"Filter my schema conformity dataset by valid_records between 5 and 100, and also show me how valid_records changed between adjacent rows — I have about 50 rows with date, row_id, dataset_group, valid_records, and checked_records.","exampleUseCases":[{"title":"Identify out-of-range validation records","prompt":"I have a list of 80 schema conformity records with valid_records and checked_records values. Can you filter to only show me rows where valid_records falls between 20 and 500, and also tell me how valid_records shifted from one row to the next?"},{"title":"Audit consecutive changes in checked records","prompt":"Here are 120 rows of daily schema conformity data across two dataset groups. Run a numeric interval filter on checked_records between 0 and 200, and report the adjacent row changes so I can see where checked_records jumped or dropped significantly."},{"title":"Spot anomalies in conformity batch data","prompt":"I've got a conformity dataset for group-a and group-b spanning 60 rows. Filter on valid_records between 10 and 1000 and also give me the row-by-row change report for valid_records so I can spot any sudden anomalies."}],"resultDescription":"Returns two reports: (1) rows whose specified field (valid_records or checked_records) falls within the supplied minimum–maximum interval, and (2) adjacent row change analysis showing the difference in that field between consecutive rows — all computed locally against the supplied data, up to 200 rows.","failureModes":["Rows array missing or empty — minimum 1 row required","Row missing required fields (row_id, dataset_group, date, valid_records, checked_records) — schema validation error","Date field does not match YYYY-MM-DD pattern — rejected","More than 200 rows submitted — exceeds maximum","Minimum greater than maximum — may return empty interval filter results","Payment of 0.01 USDC not completed — 402 response blocking access"],"whenToPreferThis":"Choose this endpoint when you have pre-collected schema conformity records (valid_records, checked_records) and need both a numeric range filter and an adjacent row change report in a single call, without needing live data lookup, predictions, or external data enrichment. It is particularly suited for data quality pipelines where you want to isolate records within acceptable bounds and simultaneously audit sequential row shifts.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:47:04.772Z","isFirstParty":false,"canonicalSlug":"schema-conformity-numeric-interval-filter-adjacent-row-changes-4e94221c"}