{"uid":"cap_Xu0sbMMeNLCp9ZmvSP42K","slug":"numeric-precision-comparisons-record-quality-audit-string-5999c0cb","name":"Numeric Precision Comparisons — Record Quality Audit + String Normalisation + Category Frequencies","description":"Numeric precision comparisons — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied numeric precision comparisons records (source_value, transformed_value). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-195?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_maxOU5oOklQ3SPEU3QCjK","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":"Runs three local analytical reports (record quality audit, string normalisation, and category frequencies) on up to 200 rows of numeric source/transformed value pairs grouped by dataset category and date.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and category frequency report on these 4 rows I have — row-1 in group-a on 2026-10-01 with source 10 and transformed 1, row-2 in group-b on 2026-10-02 with source 20 and transformed 2, row-3 in group-a on 2026-10-03 with source 15 and transformed 3, and row-4 in group-b on 2026-10-04 with source 30 and transformed 4?","exampleUseCases":[{"title":"ETL pipeline precision loss audit","prompt":"I ran an ETL job that transformed raw numeric sales figures — can you audit up to 200 rows of source vs transformed values grouped by region and date, and flag any precision mismatches or quality issues across the three reports?"},{"title":"Category frequency check after data migration","prompt":"After migrating our dataset, I want to know how often each dataset_group appears in my 150 records and whether any source or transformed values look malformed — can you run the category frequency and record quality reports on them?"},{"title":"String normalisation review for numeric columns","prompt":"Some of our transformed_value fields came back oddly formatted — can you run the string normalisation report on these 80 rows with source and transformed numeric values so I can see which ones need cleaning before we load them into the warehouse?"}],"resultDescription":"Returns three independent analytical reports covering: (1) a record quality audit flagging nulls, out-of-range values, and row-level anomalies; (2) a string normalisation report highlighting formatting inconsistencies in numeric representations; and (3) a category frequency summary showing how often each dataset_group appears across the supplied rows. All analysis is performed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error — trim input to 200 rows maximum","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) on any row causes rejection","Invalid date format (not YYYY-MM-DD) triggers a schema validation failure","Numeric values outside the ±1,000,000,000,000 range are rejected","Empty rows array returns an error — at least 1 row is required"],"whenToPreferThis":"Choose this endpoint when you need three bundled local data-quality reports (quality audit, string normalisation, category frequencies) on numeric source/transformed value pairs in a single low-cost call. It is ideal for ETL validation, post-migration checks, and data profiling workflows where you have up to 200 rows and want offline analysis without external API dependencies. Prefer alternatives if you need live data lookup, predictive modelling, or more than 200 rows processed at once.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:11.359Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-string-5999c0cb"}