{"uid":"cap_jTeQHtcCApqjh4Td_5fYw","slug":"numeric-precision-comparisons-record-quality-audit-string-018da037","name":"Numeric Precision Comparisons — Record Quality Audit, String Normalisation & Exact Row Deduplication","description":"Numeric precision comparisons — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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-20-191?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_17onva8OjDEedz5cyiN1x","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 exact row deduplication) on up to 200 rows of numeric source-vs-transformed value pairs, with no external lookups or live data.","exampleAgentPrompt":"Can you run the three-report data quality bundle — record quality audit, string normalisation, and exact row deduplication — on my numeric precision comparison dataset? I have rows with fields like row_id, dataset_group, date, source_value, and transformed_value, for example row-1 in group-a on 2026-10-01 with source_value 10 and transformed_value 1, up to about 50 rows total.","exampleUseCases":[{"title":"ETL pipeline output validation","prompt":"I've run a numeric transformation pipeline and want to check the output quality — can you audit the records for quality issues, normalise any string fields, and flag exact duplicates? Here are my rows: row-1, group-a, 2026-10-01, source 100.5, transformed 100.499; row-2, group-b, 2026-10-02, source 200.0, transformed 199.998. Check up to 30 rows."},{"title":"Pre-analysis deduplication check","prompt":"Before I run my analysis, can you check this batch of 80 numeric records for exact duplicate rows and give me a deduplication report? Each row has a row_id, dataset_group, date, source_value, and transformed_value — I want to make sure there are no repeated entries sneaking in."},{"title":"Data migration accuracy audit","prompt":"We just migrated a numeric dataset and I need to verify the transformation accuracy. Can you run a full record quality audit comparing source_value to transformed_value for my 120 rows, and also tell me if any rows are exact duplicates? Groups are 'legacy' and 'migrated', dates from 2025-01-01 onward."}],"resultDescription":"Three independent analytical reports generated locally: (1) a record quality audit highlighting missing, null, or out-of-range values; (2) a string normalisation report for text fields like row_id and dataset_group; and (3) an exact row deduplication report identifying repeated records across the submitted rows. No external data is fetched.","failureModes":["Exceeding 200 rows returns a validation error","Rows missing required fields (row_id, dataset_group, date, source_value, transformed_value) are rejected","Date fields not matching YYYY-MM-DD pattern cause schema validation failure","Numeric values outside the range -1,000,000,000,000 to 1,000,000,000,000 are rejected","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need a combined, cost-efficient data quality pass over a batch of up to 200 numeric source-vs-transformed records — getting quality audit, string normalisation, and deduplication all in one call for $0.01 USDC. Prefer it over single-purpose tools when you want all three analytical reports in a single request without any external API calls or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:39:44.427Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-record-quality-audit-string-018da037"}