{"uid":"cap_ED4Xs8jXXm-jWD3OUC_kx","slug":"label-review-record-quality-audit-string-normalisation-a981a1e9","name":"Label Review — Record Quality Audit + String Normalisation","description":"Label review — Record quality audit + String normalisation: Record quality audit + String normalisation 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-12-001?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_uDTWuVLfcyGAMHE8hd2me","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: a record quality audit and string normalisation, across up to 200 rows of reviewed_records and changed_labels data.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on my label review data? I have rows for group-a and group-b across dates in October 2026, each with reviewed_records and changed_labels counts — up to 50 rows total.","exampleUseCases":[{"title":"ML annotation pipeline quality check","prompt":"I have logs from our annotation team's label review sessions — each row has a date, a dataset group name, how many records were reviewed, and how many labels were changed. Can you audit these for quality issues and normalise any inconsistent string values? I have about 80 rows covering the past month."},{"title":"Dataset group label change analysis","prompt":"We ran a label review process across three dataset groups last quarter and I want to make sure the data is clean before we use it for training. Can you do a record quality audit and string normalisation on these 120 rows of reviewed_records and changed_labels?"},{"title":"Daily label review export validation","prompt":"Here are our daily label review exports for the past two weeks — each row has a row_id, dataset_group, date, number of reviewed_records, and changed_labels. Please run the quality audit and string normalisation reports so I can spot any bad data before I hand this off to the modelling team."}],"resultDescription":"Two independent analytical reports generated locally: (1) a record quality audit covering issues in the reviewed_records and changed_labels fields, and (2) a string normalisation report covering dataset_group and other string fields — all computed for up to 200 supplied rows without any external lookups or predictions.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, reviewed_records, changed_labels) — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Numeric values out of allowed range (-1,000,000,000,000 to 1,000,000,000,000) — rejected","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need fast, offline-style analytical reports on label review data — specifically a quality audit of reviewed_records/changed_labels and string normalisation — for up to 200 rows at a very low flat cost ($0.01 USDC for both reports). Prefer this over general-purpose data tools when your data fits the label review schema and you want deterministic, reproducible local analysis with no external API calls or model inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:12.805Z","isFirstParty":false,"canonicalSlug":"label-review-record-quality-audit-string-normalisation-a981a1e9"}