{"uid":"cap_XLFOqKGfKdc-qE-rKnLLl","slug":"numeric-precision-comparisons-distribution-adjacent-row-changes-95a228d3","name":"Numeric Precision Comparisons — Distribution & Adjacent Row Changes Analyzer","description":"Numeric precision comparisons — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes 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-20-077?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_yI1myhYA1foS9qW-tJ8Qe","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 up to 200 rows of numeric source/transformed value pairs, producing two reports: a numeric distribution summary and an adjacent-row change analysis.","exampleAgentPrompt":"Can you run the numeric distribution and adjacent-row change analysis on these 50 rows of source and transformed values from group-a — use the source_value field — so I can see how the data spreads and where big jumps occur between consecutive rows?","exampleUseCases":[{"title":"ETL pipeline numeric validation","prompt":"I have 120 rows from my data pipeline with original and transformed currency values dated from 2025-01-01 to 2025-04-30, all in group 'fx-transform'. Run the numeric distribution and adjacent-row changes report on the transformed_value field so I can see if the transformation is consistent."},{"title":"Quality check on sensor readings","prompt":"Here are 80 rows of IoT sensor readings before and after calibration, grouped as 'sensor-batch-7'. Analyze the source_value distribution and flag any big jumps between adjacent rows so I can spot calibration errors."},{"title":"Financial model output review","prompt":"I've got 150 rows of model input and output figures from our pricing engine, labeled dataset_group 'pricing-model-v2'. Give me the numeric distribution and adjacent-row change analysis on the transformed_value column so we can validate the model's output stability."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution report covering statistical spread, percentiles, min/max, mean, and similar metrics for the selected field across all supplied rows; (2) an adjacent-row change report highlighting differences between consecutive rows ordered by date, useful for detecting anomalies or trend breaks. Both reports are computed locally without external lookups.","failureModes":["Rows array is empty or missing — request rejected","More than 200 rows submitted — exceeds maximum and will error","Invalid date format (not YYYY-MM-DD) — schema validation failure","source_value or transformed_value outside ±1,000,000,000,000 — rejected","Invalid field enum value (not 'source_value' or 'transformed_value') — schema error","Missing required row fields (row_id, dataset_group, date) — validation failure"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 numeric records with source and transformed values and need both a statistical distribution summary and a sequential change analysis in a single low-cost call. It is ideal for ETL validation, data pipeline QA, and precision-transformation auditing where no external data sources or predictions are needed — everything is computed analytically from your supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:08.980Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-distribution-adjacent-row-changes-95a228d3"}