{"uid":"cap_tp2UQm8OqzbElaweSd-7S","slug":"data-reconciliation-string-normalisation-category-frequencies-46dbf75b","name":"Data Reconciliation: String Normalisation + Category Frequencies","description":"Data reconciliation — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied data reconciliation records (source_total, target_total). 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-11-024?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_total","target_total"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"target_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_total":10,"target_total":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_total":20,"target_total":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_total":15,"target_total":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_total":30,"target_total":4,"dataset_group":"group-b"}],"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_XDb7m51X2_HaxUhtIVuWU","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 data reconciliation rows (source_total vs target_total) by producing two reports: string normalization of text fields and category frequency distributions across dataset groups.","exampleAgentPrompt":"Can you run string normalization and category frequency analysis on my reconciliation data? I have rows with row_id, date (like 2026-10-01), dataset_group (e.g. 'group-a' or 'group-b'), source_total, and target_total — about 50 rows total — and I need both reports in one call.","exampleUseCases":[{"title":"Monthly reconciliation group frequency audit","prompt":"I have 120 reconciliation records from October with fields like row_id, date, dataset_group, source_total, and target_total. Can you normalize the string fields and tell me the frequency breakdown of each dataset group so I can spot if any category is over- or under-represented?"},{"title":"Data pipeline source-target comparison cleanup","prompt":"We're comparing source and target totals across two dataset groups — 'system-a' and 'system-b' — for about 80 rows dated between 2026-09-01 and 2026-09-30. Can you normalize the text fields and give me category frequency counts to check for inconsistencies before we finalize the report?"},{"title":"QA check on reconciliation export before handoff","prompt":"Before I send this reconciliation file to the finance team, can you run string normalization and category frequency analysis on these 40 rows? Each row has a row_id, a date, a dataset_group label, and source and target totals I want cleaned up and summarized by group."}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) a string normalization report that standardizes and cleans text fields such as row_id and dataset_group, and (2) a category frequency report showing the count and distribution of each distinct dataset_group value across all submitted rows. No external lookups or predictions are made — all analysis is performed locally on the supplied data.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, source_total, target_total, dataset_group) cause row-level rejection","Dates not matching YYYY-MM-DD pattern fail schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need both string normalization and category frequency analysis on reconciliation data (source vs target totals) in a single API call for up to 200 rows. It is ideal for data quality workflows that require text field cleanup and group distribution summaries together without any external data lookups or predictions. If you only need one report type or have more than 200 rows, consider alternative endpoints.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:10.876Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-string-normalisation-category-frequencies-46dbf75b"}