{"uid":"cap_fmszwA_HrcFyYHY8dZo8y","slug":"label-review-string-normalisation-numeric-row-ordering-59d4c85d","name":"Label Review — String Normalisation + Numeric Row Ordering","description":"Label review — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied label review records (reviewed_records, changed_labels). 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-12-025?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","reviewed_records","changed_labels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"changed_labels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"reviewed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","changed_labels":1,"reviewed_records":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","changed_labels":2,"reviewed_records":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","changed_labels":3,"reviewed_records":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","changed_labels":4,"reviewed_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["reviewed_records","changed_labels"],"type":"string","default":"reviewed_records"}}},"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__lozs-LjKJMj02k5tARrD","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 two local analytical reports on label review data: string normalisation of group/ID fields and numeric row ordering of reviewed_records or changed_labels, for up to 200 rows.","exampleAgentPrompt":"Can you run string normalisation and numeric row ordering on my label review data? I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, reviewed_records values of 10, 20, 15, 30, and changed_labels of 1, 2, 3, 4 — please analyse the reviewed_records field.","exampleUseCases":[{"title":"Audit label consistency across dataset groups","prompt":"I have a label review log with 50 rows across two dataset groups — can you normalise the string labels and show me how the reviewed_records are ordered numerically across all rows?"},{"title":"Rank annotation sessions by changed labels","prompt":"Here are 120 rows from our annotation pipeline with row IDs, dates, dataset groups, reviewed_records, and changed_labels — run numeric row ordering on the changed_labels field and also normalise the string columns for me."},{"title":"Validate label review export before handoff","prompt":"Before I hand off this label review export to the data team, can you run string normalisation and numeric ordering on the reviewed_records column across these 30 rows so I can catch any formatting issues or unexpected ordering?"}],"resultDescription":"Returns two analytical reports computed locally: (1) a string normalisation report covering row_id and dataset_group label formatting, and (2) a numeric row ordering report ranking rows by the selected field (reviewed_records or changed_labels). No external lookups or predictions are made; all analysis is performed on the supplied data.","failureModes":["More than 200 rows submitted — request rejected at maxItems limit","Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Invalid field enum value (anything other than 'reviewed_records' or 'changed_labels') causes an error","Empty rows array rejected by minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline analytical reporting on label review datasets — specifically string normalisation of group/ID fields and numeric ordering of review metrics — without requiring any external data lookup, ML prediction, or live enrichment. It is ideal for data quality checks on annotation or labelling pipelines up to 200 rows, where two bundled reports at $0.01 USDC represent a cost-effective single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:21.873Z","isFirstParty":false,"canonicalSlug":"label-review-string-normalisation-numeric-row-ordering-59d4c85d"}