{"uid":"cap_YZlxlUwV1FVhaHpFO_HCP","slug":"sampling-observations-numeric-interval-filter-adjacent-row-changes-ebd4b83f","name":"Sampling Observations — Numeric Interval Filter + Adjacent Row Changes","description":"Sampling observations — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes for supplied sampling observations records (sample_value, sample_weight). 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-15-127?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_value"},"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_222znA6McfSTDMhal9Fux","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":"Applies a numeric interval filter and adjacent row change analysis to supplied sampling observation records (sample_value, sample_weight) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have 150 sampling observation rows with columns row_id, dataset_group, date, sample_value, and sample_weight — can you filter them to only rows where sample_value falls between 5 and 95, and also compute adjacent row changes for sample_value, giving me both reports?","exampleUseCases":[{"title":"Survey response range filtering","prompt":"I've got survey data with 80 rows — each has a row_id, group label, date, sample_value, and sample_weight. Can you filter to rows where sample_value is between 10 and 500, and tell me how the sample_value changes between adjacent rows?"},{"title":"Quality control on lab measurements","prompt":"Here are 120 lab measurement records with sample_value and sample_weight fields. Filter them to the interval between -50 and 200 for sample_weight, and show me the adjacent row changes so I can spot sudden jumps."},{"title":"Time-series sampling anomaly detection","prompt":"I have 60 daily sampling observations grouped by dataset_group. For the sample_value field, give me all rows between 0 and 75 and flag the adjacent row differences so I can see where big shifts happen day to day."}],"resultDescription":"Returns two local analytical reports: (1) rows from the supplied dataset whose target field (sample_value or sample_weight) falls within the specified [minimum, maximum] interval, and (2) adjacent row change analysis showing value differences between consecutive rows — all computed offline on the supplied data, no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required row fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation error","minimum greater than maximum produces empty or invalid interval filter results","Non-date-formatted date strings fail schema pattern validation","Payment of 0.01 USDC not included or declined via x402 protocol blocks execution","Null sample_value or sample_weight values may affect change computation output"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation records with sample_value and sample_weight fields and need two offline analytical operations — numeric range filtering and adjacent row change detection — in a single low-cost call. Prefer it over general analytics platforms when you want a deterministic, privacy-safe local computation with no external data lookups and a predictable $0.01 flat cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:57.030Z","isFirstParty":false,"canonicalSlug":"sampling-observations-numeric-interval-filter-adjacent-row-changes-ebd4b83f"}