{"uid":"cap_1fjKdIVFqsmFy1Y42fNXW","slug":"feature-availability-analytics-adjacent-row-changes-equal-width-c553942e","name":"Feature Availability Analytics — Adjacent Row Changes & Equal-Width Histogram","description":"Feature availability — Adjacent row changes + Equal-width histogram: Adjacent row changes + Equal-width histogram 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-10-156?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_V2HuYEvw1CkyvlmSizI7I","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 feature availability data: adjacent row-to-row change detection and an equal-width histogram, for up to 200 rows.","exampleAgentPrompt":"Can you run adjacent row change detection and an equal-width histogram (10 bins) on my populated_features column for these 50 feature availability records grouped by dataset_group and dated from 2024-01-01 to 2024-02-19?","exampleUseCases":[{"title":"Daily feature completeness drift detection","prompt":"I have 150 rows of daily feature availability logs with populated_features and expected_features from 2023-06-01 to 2023-10-28. Can you run adjacent row changes on populated_features to see how feature completeness shifts day over day?"},{"title":"Histogram of feature population rates","prompt":"Can you build an equal-width histogram with 8 bins on the expected_features column across my 90 dataset records grouped by group-a and group-b to understand how expected feature counts are distributed?"},{"title":"ML pipeline feature coverage audit","prompt":"I need to audit feature coverage for my ML pipeline — here are 200 rows with populated_features and expected_features per dataset group and date. Run both the adjacent row changes and a 5-bin histogram on populated_features so I can spot gaps."}],"resultDescription":"Two analytical reports: (1) adjacent row changes showing delta values between consecutive rows for the chosen field, and (2) an equal-width histogram with up to 30 configurable bins summarizing the distribution of populated_features or expected_features across the supplied records.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) causes row validation failure","bins value outside 1–30 range returns an error","Null values in the selected field may affect histogram accuracy or row change calculations","Missing required row fields (row_id, date, populated_features, expected_features) cause schema validation failure","Payment of 0.01 USDC not completed results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical views — row-to-row change detection AND a distribution histogram — on feature availability data (populated vs expected features) without any external lookup or live data fetch. Ideal for data quality audits, ML feature store monitoring, and dataset completeness reporting at low cost ($0.01 for both reports on up to 200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:35.522Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-adjacent-row-changes-equal-width-c553942e"}