{"uid":"cap_WIfAUw4oGw-3yYywEjNMW","slug":"numeric-precision-comparisons-numeric-distribution-daily-sum-series-8cef140e","name":"Numeric Precision Comparisons — Numeric Distribution + Daily Sum Series","description":"Numeric precision comparisons — Numeric distribution + Daily sum series: Numeric distribution + Daily sum series for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-075?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"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_Bite_OuUSUxHRoc03EGn2","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":"Analyzes user-supplied numeric precision comparison data (source vs. transformed values) to produce two local analytical reports: a numeric distribution breakdown and a daily sum time series.","exampleAgentPrompt":"Can you run a numeric distribution and daily sum series analysis on these precision comparison rows — I have source_value and transformed_value pairs across dates in group-a and group-b, up to 200 rows — and analyze the source_value field?","exampleUseCases":[{"title":"ETL pipeline precision audit","prompt":"I have up to 150 rows of before-and-after numeric values from my ETL pipeline — each row has a date, a dataset_group like 'pipeline-a', and both source_value and transformed_value. Can you give me a numeric distribution and daily sum series on the source_value field so I can see where precision is being lost?"},{"title":"Financial rounding error analysis","prompt":"I'm tracking rounding differences in our financial calculations — I have a table with dates, group labels, original amounts as source_value, and rounded amounts as transformed_value for about 80 records. Can you run both the distribution report and daily sum series on the transformed_value field?"},{"title":"ML feature transformation QA","prompt":"We're QA-ing a feature transformation step in our ML pipeline and I want to compare raw vs. scaled values. I have around 200 rows with row IDs, dates, dataset_group set to 'feature-set-1', and source_value and transformed_value columns. Can you generate the numeric distribution and daily sum series reports analyzing source_value?"}],"resultDescription":"Returns two independent analytical reports computed locally on the supplied rows: (1) a numeric distribution report showing statistical spread, frequency buckets, and key percentiles for the selected field; and (2) a daily sum series aggregating the selected field's values by date. Both reports cover up to 200 input rows with no external lookups or live data.","failureModes":["Fewer than 1 row supplied — minimum row count not met","More than 200 rows submitted — exceeds maximum batch size","Missing required fields (row_id, dataset_group, date, source_value, or transformed_value) on any row","Date field does not match YYYY-MM-DD pattern — validation error","Numeric values outside allowed range (-1e12 to 1e12) — schema violation","Invalid field selector enum value (must be 'source_value' or 'transformed_value')","All values null for selected field — distribution and sum series cannot be computed"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of paired numeric data (source and transformed values) with date and group labels and want two bundled analytical reports — distribution and daily sum series — computed locally with no external dependencies, all for $0.01 USDC. It is ideal for data quality audits, ETL validation, or transformation QA where you need a quick statistical snapshot rather than real-time lookups, predictions, or external enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:18.009Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-numeric-distribution-daily-sum-series-8cef140e"}