{"uid":"cap_elD5Ncm9w24tYyznbrpq1","slug":"numeric-precision-comparisons-record-quality-audit-category-frequencies-d6ca4937","name":"Numeric Precision Comparisons — Record Quality Audit + Category Frequencies","description":"Numeric precision comparisons — Record quality audit + Category frequencies: Record quality audit + Category frequencies 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-20-006?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}}},"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_8k0_ID018nP52mMI1U3im","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 source/transformed numeric pairs, producing a record quality audit report and a category frequency report using local analytical engines.","exampleAgentPrompt":"Can you run a quality audit and category frequency report on these numeric pairs — I have up to 200 rows with source values and their transformed counterparts, each tagged with a dataset group and date, and I need to see where the precision looks off and how the records break down by group?","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"I just ran a batch transformation on our pricing data — here are the original and transformed values for about 150 rows, each with a row ID, group label, and date. Can you audit the record quality and show me how many records fall into each dataset group?"},{"title":"Validating numeric precision after unit conversion","prompt":"We converted a set of sensor readings from imperial to metric and I want to make sure nothing went wrong. Here are up to 200 rows with the before and after values labeled by sensor group and date — run a quality audit and give me category frequencies so I can spot any weird groups."},{"title":"Reviewing data transformation consistency by category","prompt":"Can you analyze these 80 rows of financial data where source_value is the raw ledger amount and transformed_value is after rounding? Group them by account_category and date, and give me both a quality audit and a breakdown of how many records are in each category."}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit identifying issues such as null values, mismatches, or precision anomalies across source and transformed numeric pairs, and (2) a category frequency summary showing how many records appear in each dataset_group. Both reports are computed locally with no external lookups.","failureModes":["Empty or malformed rows array returns a validation error","Rows exceeding 200 items are rejected (maxItems limit)","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause schema validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Malformed date strings not matching YYYY-MM-DD pattern fail validation","Payment failure (insufficient USDC balance) prevents execution"],"whenToPreferThis":"Choose this endpoint when you need a quick, offline quality audit of numeric source-to-transformed value pairs alongside category frequency counts, without requiring external data lookups or professional assessments. It is ideal for ETL validation, data transformation QA, and categorical breakdowns where all data is supplied inline and results are needed fast. It covers both quality and frequency in a single $0.01 call for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:29.949Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-category-frequencies-d6ca4937"}