{"uid":"cap_pThMSOOwPRkNYiMplK-y0","slug":"feature-availability-analytics-record-quality-audit-string-f467c8be","name":"Feature Availability Analytics — Record Quality Audit, String Normalisation & IQR Outlier Flags","description":"Feature availability — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied feature availability records (populated_features, expected_features). 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-10-203?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"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},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"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_RRg7hohTF5l_1qCS-KUM6","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 local analytical reports on feature availability records: a record quality audit, string normalisation, and IQR-based outlier detection — all against up to 200 supplied rows.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and IQR outlier check on my feature availability records? I have rows with row_id, dataset_group, date, populated_features, and expected_features — here are up to 200 rows — and I want to analyse the populated_features field.","exampleUseCases":[{"title":"ML dataset completeness audit","prompt":"I have a batch of feature availability records from my ML pipeline — each row has a row_id, dataset_group, date, and counts for populated_features and expected_features. Can you audit the record quality, normalise any string fields, and flag IQR outliers in the populated_features column across all 150 rows?"},{"title":"Outlier detection in expected feature counts","prompt":"Run an IQR outlier analysis on my expected_features column across these 80 feature availability records — I also want the record quality audit and string normalisation reports at the same time. Each row has a row_id, dataset_group, and date."},{"title":"Data pipeline QA before model training","prompt":"Before I feed this feature availability dataset into training, can you do a full quality audit, normalise the string fields, and flag any IQR outliers in expected_features? I've got around 200 rows with row_id, dataset_group, date, populated_features, and expected_features fields."}],"resultDescription":"Returns three independent analytical reports for up to 200 supplied rows: (1) a record quality audit highlighting missing, malformed, or incomplete records; (2) a string normalisation report identifying and correcting inconsistencies in text fields like dataset_group; and (3) an IQR outlier flag report marking rows where the selected numeric field (populated_features or expected_features) falls outside interquartile range thresholds. All processing is performed locally without external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, populated_features, expected_features) cause row-level errors or rejection","Dates not matching YYYY-MM-DD pattern are rejected by schema validation","Null values in numeric fields may reduce outlier detection accuracy","Invalid field enum value (anything other than populated_features or expected_features) causes a request error"],"whenToPreferThis":"Choose this endpoint when you need three standard data quality reports (quality audit, string normalisation, IQR outlier detection) on feature availability records in a single low-cost call. It is ideal for ML pipeline QA, pre-training dataset validation, or routine monitoring of feature completeness metrics. It does not perform live lookups, predictions, or external integrations, so prefer alternatives if you need real-time data enrichment or model-based anomaly detection.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:41.140Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-record-quality-audit-string-f467c8be"}