{"uid":"cap_q6M5XMjXG8ISt7UK_m8oi","slug":"feature-availability-analytics-category-frequencies-keyed-dataset-e254c958","name":"Feature Availability Analytics: Category Frequencies + Keyed Dataset Comparison","description":"Feature availability — Category frequencies + Keyed dataset comparison: Category frequencies + Keyed dataset comparison 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-10-111?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}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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":21},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":17},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":33}],"maxItems":200,"minItems":0}}},"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_DxSHmSPspsxBnPqzNDo4L","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: category-level frequency distributions and a keyed comparison between two datasets (rows vs other), covering populated vs expected feature counts across up to 200 rows.","exampleAgentPrompt":"I have two batches of feature availability records — each with row IDs, dataset groups, dates, and populated vs expected feature counts — can you run a category frequency breakdown and a keyed comparison between them to see where the differences are? Here's the primary dataset: [{\"row_id\":\"r1\",\"dataset_group\":\"group-a\",\"date\":\"2025-01-01\",\"populated_features\":80,\"expected_features\":100}] and the comparison dataset: [{\"row_id\":\"r1\",\"dataset_group\":\"group-a\",\"date\":\"2025-01-01\",\"populated_features\":85,\"expected_features\":100}].","exampleUseCases":[{"title":"ML feature store completeness audit","prompt":"I have two versions of my ML feature store snapshot — this week's and last week's — both with row IDs, dataset groups, dates, populated feature counts, and expected feature counts. Can you run a category frequency report and a keyed dataset comparison so I can see which groups degraded in feature availability?"},{"title":"Dataset migration feature coverage check","prompt":"We just migrated data from system A to system B and I need to verify feature coverage didn't drop. I have up to 200 rows from each system with populated_features and expected_features per dataset_group and date — can you give me a frequency breakdown by category and a side-by-side comparison of the two datasets keyed by row ID?"},{"title":"Daily feature availability monitoring","prompt":"Every morning I want to compare today's feature availability records against yesterday's baseline — both have row_id, dataset_group, date, populated_features, and expected_features fields — and I need category-level frequency counts plus a keyed diff report to catch any sudden drops in feature population."}],"resultDescription":"Returns two analytical reports: (1) a category frequency distribution showing how feature availability records are distributed across dataset_group categories, and (2) a keyed comparison between the primary (rows) and secondary (other) datasets, highlighting differences in populated_features and expected_features matched by row_id. No external data sources are used; all computation is local and deterministic.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level validation errors","Null values in populated_features or expected_features may affect comparison accuracy","Date strings not matching YYYY-MM-DD pattern will fail schema validation","Mismatched row_ids between primary and comparison datasets result in unmatched rows in the keyed comparison","Empty input array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you need both a category-level frequency breakdown and a keyed row-by-row comparison of two feature availability datasets in a single low-cost call. It is ideal for data quality monitoring, ML feature store audits, and dataset migration validation where you have structured records with populated vs expected feature counts grouped by dataset category. Prefer this over single-report endpoints when you need both analyses at once for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:41.635Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-category-frequencies-keyed-dataset-e254c958"}