{"uid":"cap_Fv9oLBtwgKASEZ8WefYnX","slug":"feature-availability-analytics-numeric-distribution-date-by-category-07a1a89e","name":"Feature Availability Analytics — Numeric Distribution + Date by Category Pivot","description":"Feature availability — Numeric distribution + Date by category pivot: Numeric distribution + Date by category pivot 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-10-083?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_kspMPgcXfKmKnmBdEheTi","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 supplied feature availability data: a numeric distribution summary and a date-by-category pivot, for up to 200 rows.","exampleAgentPrompt":"I have 4 rows of feature availability data — group-a on 2026-10-01 with 10 populated and 1 expected, group-b on 2026-10-02 with 20 populated and 2 expected, group-a on 2026-10-03 with 15 populated and 3 expected, and group-b on 2026-10-04 with 30 populated and 4 expected — can you run a numeric distribution and date-by-category pivot on the populated_features field?","exampleUseCases":[{"title":"ML dataset feature completeness audit","prompt":"I have a list of 50 dataset records across three groups with dates and counts of populated vs expected features — can you give me a numeric distribution and a pivot by group and date so I can see where features are missing?"},{"title":"Data pipeline quality monitoring","prompt":"Here are 120 rows from our data pipeline tracking populated_features and expected_features by dataset_group and date — run both the numeric distribution report and the date-by-category pivot on expected_features so I can spot anomalies."},{"title":"Feature store coverage review","prompt":"I've got feature store audit records for the last quarter, each with a group label, date, and counts of populated and expected features — give me the numeric distribution and category-date pivot on populated_features for all 80 rows."}],"resultDescription":"Two independent analytical reports: (1) a numeric distribution of the selected field (populated_features or expected_features) showing statistical spread across the supplied rows, and (2) a date-by-category pivot table breaking down values by dataset_group and date. Both reports are computed locally from the submitted data with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (-1T to 1T) — rejected","Invalid field enum value (not populated_features or expected_features) — error"],"whenToPreferThis":"Choose this endpoint when you need fast, offline statistical analysis of feature availability records — specifically a numeric distribution plus a date-by-category pivot — without any external API calls, predictions, or live data lookups. Ideal for auditing ML dataset completeness, monitoring data pipeline feature coverage, or reviewing feature store population rates across groups over time. Best for batches of up to 200 rows where both reports are needed simultaneously at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:06.610Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-distribution-date-by-category-07a1a89e"}