{"uid":"cap_fsKnGWadce0PPC1x6oUU1","slug":"data-ingestion-batches-string-normalisation-category-frequencies-96417e81","name":"Data Ingestion Batches — String Normalisation + Category Frequencies","description":"Data ingestion batches — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied data ingestion batches records (received_rows, accepted_rows). 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-01-024?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"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_zu2PnO1W2VieFpVHleYFw","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":"Analyses up to 200 data ingestion batch records to produce two local reports: normalised string representations of row fields and category frequency distributions across dataset groups.","exampleAgentPrompt":"Can you normalise the string fields and compute category frequencies for my ingestion batch data? I have 4 rows: row-1 on 2026-10-01 for group-a with 10 received and 1 accepted, row-2 on 2026-10-02 for group-b with 20 received and 2 accepted, row-3 on 2026-10-03 for group-a with 15 received and 3 accepted, and row-4 on 2026-10-04 for group-b with 30 received and 4 accepted.","exampleUseCases":[{"title":"Audit dataset group distribution in pipeline","prompt":"I have a week of ingestion batch logs across three dataset groups — can you compute the frequency of each group and normalise the string labels? Here are my 10 rows with row IDs, dates, group names, and received/accepted counts."},{"title":"Spot inconsistent group naming in batches","prompt":"My ingestion pipeline uses group names like 'Group-A', 'group_a', and 'GROUP A' — can you normalise those strings and tell me the frequency of each distinct category across these 50 batch records I'm sending?"},{"title":"Summarise ingestion acceptance by category","prompt":"I want to understand how my accepted rows are distributed by dataset group — please run string normalisation and category frequency analysis on these 200 ingestion batch records I've collected this month."}],"resultDescription":"Two analytical reports generated from the supplied batch records: (1) a string normalisation report standardising text fields such as row_id and dataset_group, and (2) a category frequency report showing counts and distributions of values across dataset groups — all computed locally with no external lookups or predictions.","failureModes":["More than 200 rows submitted — maxItems limit exceeded, request rejected","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) causes validation failure","Date strings not matching YYYY-MM-DD pattern cause schema rejection","Numeric values outside the allowed range (±1,000,000,000,000) cause validation errors","Empty rows array (minItems: 1 not met) causes request failure","Payment of 0.01 USDC not fulfilled causes 402 response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 data ingestion batch records and need two specific lightweight analytical outputs — string normalisation and category frequency distributions — without any external data lookups, predictions, or professional assessments. It is ideal for quick, local, cost-effective batch analytics on ingestion pipeline data when you already have structured row-level records with group labels and row counts.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:24.240Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-string-normalisation-category-frequencies-96417e81"}