{"uid":"cap_O0Qt_BlVtozH0VakfFDKy","slug":"numeric-precision-comparisons-string-normalisation-adjacent-row-changes-b36f6fde","name":"Numeric Precision Comparisons — String Normalisation + Adjacent Row Changes","description":"Numeric precision comparisons — String normalisation + Adjacent row changes: String normalisation + 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-20-029?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_RBlVYpHpy5Vc4mB4aEAkG","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 value pairs, producing two local analytical reports: string normalisation of numeric values and adjacent row change detection.","exampleAgentPrompt":"Can you run a string normalisation and adjacent row changes analysis on these 4 rows of source vs transformed values — row-1 on 2026-10-01 with source 10 and transformed 1 in group-a, row-2 on 2026-10-02 with source 20 and transformed 2 in group-b, row-3 on 2026-10-03 with source 15 and transformed 3 in group-a, and row-4 on 2026-10-04 with source 30 and transformed 4 in group-b — using source_value as the field?","exampleUseCases":[{"title":"ETL pipeline precision audit","prompt":"I have a batch of 50 rows from our ETL pipeline — each row has an original source value and the transformed output value, tagged by date and dataset group. Can you run string normalisation and adjacent row change detection on the source_value field so I can spot where precision is being lost between steps?"},{"title":"Time series anomaly detection","prompt":"Here are 120 daily readings across two product groups — each row has a row ID, date, source measurement, and the transformed version. Please analyze adjacent row changes on the transformed_value field so I can see where big jumps or drops occurred day over day."},{"title":"Data migration validation","prompt":"We just migrated our numeric records and I want to check the before-and-after values. I have 80 rows with source_value as the pre-migration number and transformed_value as the post-migration number. Can you do a string normalisation and adjacent row changes report on source_value for these rows grouped by their dataset categories?"}],"resultDescription":"Two analytical reports for the submitted rows: (1) a string normalisation report showing how numeric values are standardised in text form, and (2) an adjacent row changes report showing value differences between consecutive rows. Results are computed locally from the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) causes rejection","Dates not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array is rejected","Invalid field enum value (not source_value or transformed_value) causes error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, cost-effective local computation on up to 200 rows of numeric source/transformed pairs — specifically for string normalisation and adjacent row change reports — without requiring external data lookups, predictions, or professional assessments. Ideal for ETL validation, data migration checks, or time-series step analysis within a single API call at $0.01 USDC for two bundled reports.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T06:44:33.766Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-adjacent-row-changes-b36f6fde"}