{"uid":"cap_wNFPgESpWWVubRueSKMma","slug":"feature-availability-analytics-string-normalisation-numeric-ef163ff3","name":"Feature Availability Analytics — String Normalisation + Numeric Distribution","description":"Feature availability — String normalisation + Numeric distribution: String normalisation + Numeric distribution for supplied feature availability records (populated_features, expected_features). 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-10-022?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_H6v9DsfiS-ZsUHdRky7BU","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 (string normalisation and numeric distribution) on up to 200 rows of feature availability data (populated_features, expected_features).","exampleAgentPrompt":"Can you run string normalisation and numeric distribution reports on my feature availability data? I have up to 50 rows with row_id, dataset_group, date, populated_features, and expected_features fields — analyze the populated_features column.","exampleUseCases":[{"title":"Feature completeness profiling for ML pipeline","prompt":"I have a dataset of 120 feature availability records across two groups — 'train' and 'test' — with dates ranging through Q3 2025. Can you run the string normalisation and numeric distribution reports on the populated_features column so I can see how complete my feature set is?"},{"title":"Comparing expected vs populated feature counts","prompt":"We're auditing our data pipeline and I need to understand the numeric distribution of expected_features across 80 rows grouped by dataset. Run both the string normalisation and numeric distribution reports on the expected_features field for me."},{"title":"Daily feature availability trend analysis","prompt":"I've got daily feature availability records for two dataset groups over the past month — about 60 rows total. Can you analyze the populated_features column with string normalisation and numeric distribution so I can spot any anomalies in how features are being populated day by day?"}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report summarising text-form values in the selected feature column, and (2) a numeric distribution report with statistical summaries (e.g. min, max, mean, percentiles) of the numeric feature values — all computed locally against the supplied rows with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, populated_features, expected_features) causes rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Invalid field enum value (not 'populated_features' or 'expected_features') fails validation"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical profiling of feature availability data — specifically both string normalisation and numeric distribution in a single call — without requiring external data lookups, predictions, or professional assessments. Ideal for data pipeline audits, ML feature completeness checks, and dataset quality reviews on batches of up to 200 rows at very low cost ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:41.051Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-string-normalisation-numeric-ef163ff3"}