{"uid":"cap_vBrZIDs49Z3AsKyKFVsik","slug":"label-review-analytics-record-quality-audit-string-normalisation-328b88f5","name":"Label Review Analytics — Record Quality Audit, String Normalisation & Category Frequencies","description":"Label review — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-195?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_6ep5IphSSq2Uj02lrdb18","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 three analytical reports on label review data: record quality audit, string normalisation, and category frequency analysis for up to 200 rows of reviewed records and changed labels.","exampleAgentPrompt":"Can you audit the quality of my label review records, normalise the strings, and show me category frequencies? I have up to 50 rows with dataset groups like 'group-a' and 'group-b', dates from 2026-10-01, reviewed_records counts, and changed_labels counts.","exampleUseCases":[{"title":"ML annotation pipeline quality check","prompt":"I have a batch of label review records from our annotation team — about 120 rows covering two dataset groups across October 2026. Can you run a record quality audit, normalise the string fields, and tell me the category frequencies for reviewed_records and changed_labels?"},{"title":"Label change rate analysis by group","prompt":"We ran a labelling sprint across three dataset groups last month. I have the daily reviewed_records and changed_labels counts — around 80 rows total. Can you analyse the category frequencies to see which group had the most label changes, and also flag any data quality issues?"},{"title":"Pre-training dataset label consistency review","prompt":"Before we hand this labelled dataset off to the ML team, I need a string normalisation report and a record quality audit on our 200-row label review export. The rows have dataset_group, date, reviewed_records, and changed_labels fields — can you generate all three reports?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging completeness, nulls, and anomalies; (2) a string normalisation report standardising text fields like dataset_group; (3) a category frequency table showing distribution counts for categorical fields across the supplied rows. All derived purely from the supplied input data with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, reviewed_records, changed_labels) — validation error","Date fields not matching YYYY-MM-DD pattern — schema validation failure","Null or out-of-range numeric values for reviewed_records or changed_labels — may produce incomplete audit results","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) bundle of three data quality and normalisation reports on label review data without requiring any external data sources, live predictions, or professional assessments. It is ideal for ML/annotation teams wanting to validate a label review export before downstream use, particularly when the dataset is under 200 rows and you want quality audit, string normalisation, and frequency analysis in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:48.505Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-record-quality-audit-string-normalisation-328b88f5"}