{"uid":"cap_b49SViiTWV4jnt0xtWEMD","slug":"label-review-analytics-record-quality-audit-string-normalisation-21a218f8","name":"Label Review Analytics — Record Quality Audit, String Normalisation & Numeric Distribution","description":"Label review — Record quality audit + String normalisation + Numeric distribution: Record quality audit + String normalisation + Numeric distribution 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-12-193?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_aDGb-OiXnh9pdjn95tI1b","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 (record quality audit, string normalisation, and numeric distribution) on up to 200 rows of label review data containing reviewed_records and changed_labels fields.","exampleAgentPrompt":"Can you run a full quality audit, string normalisation check, and numeric distribution analysis on my label review data? I have 4 rows: row-1 (2026-10-01, group-a, reviewed_records=10, changed_labels=1), row-2 (2026-10-02, group-b, reviewed_records=20, changed_labels=2), row-3 (2026-10-03, group-a, reviewed_records=15, changed_labels=3), row-4 (2026-10-04, group-b, reviewed_records=30, changed_labels=4) — run the analysis on the reviewed_records field.","exampleUseCases":[{"title":"ML annotation pipeline quality check","prompt":"I've got 50 rows of label review results from last month's annotation sprint — can you audit the record quality, normalise any string inconsistencies in dataset_group, and show me the numeric distribution of reviewed_records across groups?"},{"title":"Detecting label change anomalies","prompt":"Can you run a numeric distribution report on the changed_labels column across my label review export? I want to see if any dataset groups have unusually high or low label change rates — I have about 120 rows covering October."},{"title":"Pre-handoff data quality report","prompt":"Before I hand off this label review dataset to the modelling team, can you run all three reports — record quality audit, string normalisation, and numeric distribution — on these 80 rows so I can catch any data issues early?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging completeness and structural issues, (2) a string normalisation report identifying inconsistencies or formatting problems in text fields like dataset_group, and (3) a numeric distribution summary (min, max, mean, percentiles, etc.) for the selected numeric field (reviewed_records or changed_labels), all computed locally on the supplied data without any external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, date, reviewed_records, changed_labels) in one or more row objects","Date field does not match YYYY-MM-DD pattern — validation error","field enum value not recognised (must be reviewed_records or changed_labels)","Empty rows array — minimum 1 row required","Numeric values outside allowed range (-1e12 to 1e12) — schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need a bundled, low-cost ($0.01) triple analytical report — quality audit, string normalisation, and numeric distribution — on label review tabular data with up to 200 rows, without requiring live lookups, ML predictions, or external data enrichment. It is ideal for pre-processing validation of annotation pipelines or ML label review exports where you want three reports in a single API call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:32.353Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-record-quality-audit-string-normalisation-21a218f8"}