{"uid":"cap_caBgQFJ8sJahB2_yhg2r5","slug":"time-series-completeness-analyzer-numeric-distribution-adjacent-row-ce372040","name":"Time Series Completeness Analyzer — Numeric Distribution & Adjacent Row Changes","description":"Time series completeness — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes for supplied time series completeness records (observed_points, expected_points). 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-16-077?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"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},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"}}},"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_Vscwy2TWdRxElv4yvuIQZ","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":"Computes numeric distribution statistics and adjacent row-to-row change analysis for time series completeness records (observed vs. expected data points) across up to 200 rows.","exampleAgentPrompt":"I have daily time series completeness records for groups 'sensor-a' and 'sensor-b' from 2025-01-01 to 2025-01-10 — each row has an observed_points and expected_points value. Can you run the numeric distribution and adjacent row changes analysis on the observed_points field for all 10 rows?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I've got 60 rows of daily completeness data for my ingestion pipeline — each row has a date, dataset_group, observed_points, and expected_points. Can you run the numeric distribution and adjacent row changes reports on the observed_points field so I can see how data completeness is spread and how it shifts day to day?"},{"title":"Detecting gaps in sensor data collection","prompt":"I have 90 days of sensor data completeness records across two groups, 'sensor-north' and 'sensor-south', with observed and expected point counts per day. Please analyze the expected_points field to show me the numeric distribution and how values change between adjacent rows — I want to spot any sudden drops."},{"title":"Monthly reporting completeness QA","prompt":"Before I finalize my monthly report, I need to validate completeness across 30 date records for dataset group 'finance-feed'. Run the numeric distribution and adjacent row changes analysis on the observed_points field and tell me if anything looks off."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, spread) of the chosen field (observed_points or expected_points) across all supplied rows, and (2) an adjacent row change report showing value deltas between consecutive rows — both computed locally with no external lookups. Covers up to 200 input rows.","failureModes":["Missing required fields (row_id, date, observed_points, expected_points) causes validation error","Submitting more than 200 rows exceeds the maxItems limit and will be rejected","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Null values in both observed_points and expected_points may produce incomplete distribution stats","Selecting a field value outside the enum ('observed_points' or 'expected_points') causes a schema error"],"whenToPreferThis":"Choose this endpoint when you have structured time series completeness data (observed vs. expected point counts) and need two quick local analytical reports — numeric distribution and adjacent row changes — without any live lookups, predictions, or external integrations. It is ideal for lightweight data quality checks on up to 200 rows at minimal cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:11.282Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-numeric-distribution-adjacent-row-ce372040"}