{"uid":"cap_VlViFkFUX3-YD-lPfb9oK","slug":"label-review-analytics-string-normalisation-category-frequencies-76a22be3","name":"Label Review Analytics — String Normalisation & Category Frequencies","description":"Label review — String normalisation + Category frequencies: String normalisation + Category frequencies 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-12-024?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}}},"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_sy6M2k7VdcsOZ0slG3FQl","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":"Performs string normalisation and category frequency analysis on label review records (reviewed_records, changed_labels) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and category frequency analysis on my label review data? I have up to 50 rows across two dataset groups — 'production' and 'staging' — each with a date, reviewed_records count, and changed_labels count, and I need both reports back.","exampleUseCases":[{"title":"ML annotation audit frequency report","prompt":"I have 120 rows of label review records from our annotation pipeline — each row has a date, dataset group name, number of reviewed records, and how many labels were changed. Can you run category frequency analysis on them to see which groups have the most label changes?"},{"title":"String normalisation for label review export","prompt":"Our label review export has inconsistent string formatting across dataset groups. Can you normalise the strings in my 80-row label review dataset and give me a report showing what was standardised?"},{"title":"Dual-report label quality review","prompt":"I want both a string normalisation report and a category frequency breakdown for my label review data — I've got 200 rows with row IDs, dates, dataset groups like 'train' and 'val', plus reviewed_records and changed_labels counts for each. Can you run both analyses at once?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report covering standardisation of string fields across the supplied rows, and (2) a category frequency report showing distribution and frequencies of reviewed_records and changed_labels broken down by dataset_group and date — all computed locally without any external lookup or prediction.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, date, reviewed_records, changed_labels) cause row-level validation errors","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) rejected by schema","Empty rows array (minItems:1 not met) causes request failure","Payment of 0.01 USDC not fulfilled — endpoint returns 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline analytical processing of label review records — specifically string normalisation and category frequency computation — on datasets of up to 200 rows, without requiring live data lookups, predictions, or external API calls. It is ideal for ML teams auditing annotation quality, data engineers normalising label pipeline outputs, or analysts summarising label change rates by dataset group. At $0.01 for two reports, it is cost-effective for small batch analytical tasks on structured label review data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:59.256Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-string-normalisation-category-frequencies-76a22be3"}