{"uid":"cap_8UlFE-DMBT4XSyTNNERHN","slug":"feature-availability-analytics-adjacent-row-changes-explicit-missing-0ef3f33e","name":"Feature Availability Analytics: Adjacent Row Changes + Explicit Missing-Value Fill","description":"Feature availability — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-10-162?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_WOvt3wtMndAn_e2tEimLq","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":"Analyzes feature availability records to produce two reports: adjacent row change detection and explicit missing-value fill, using local analytical engines on up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of feature availability records with populated_features and expected_features per date and dataset group — can you run the adjacent row changes report and fill any missing populated_features values using median imputation on that data?","exampleUseCases":[{"title":"Daily feature drift monitoring","prompt":"Here are 150 rows of feature availability logs by date and group — run the adjacent row changes report on populated_features so I can see where counts jumped or dropped between consecutive days."},{"title":"Missing value imputation for pipeline audit","prompt":"I've got a dataset of expected_features and populated_features records with some nulls — fill the missing populated_features values using mean imputation and give me both the fill report and the adjacent row change report."},{"title":"Cross-group feature completeness review","prompt":"Can you analyze these 80 feature availability records across group-a and group-b — I need the adjacent row change detection and a zero-fill for any missing expected_features values so I can audit completeness?"}],"resultDescription":"Returns two analytical reports: (1) an adjacent row change report identifying shifts between consecutive rows in the specified field, and (2) a missing-value fill report showing imputed values using the chosen method (zero, mean, or median) — all computed locally on the supplied records with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Unknown fill method value (not zero/mean/median) — enum validation failure","Unknown field value (not populated_features/expected_features) — enum validation failure","Empty rows array (minItems:1 violated) — validation error","row_id missing or empty string — required field error","Numeric values out of allowed range (±1 trillion) — validation error"],"whenToPreferThis":"Choose this endpoint when you need both adjacent row change detection and missing-value imputation on feature availability data in a single call, with no need for external lookups or predictions. It is well-suited for data pipeline audits, feature engineering QA, and time-series completeness checks on small to medium datasets (up to 200 rows). Prefer this over general-purpose analytics when your data conforms to the populated_features/expected_features schema and you want two bundled reports at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:34.479Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-adjacent-row-changes-explicit-missing-0ef3f33e"}