{"uid":"cap_UAflOm0iILJZaxJJXAb5c","slug":"feature-availability-string-normalisation-adjacent-row-changes-f2a17721","name":"Feature Availability — String Normalisation + Adjacent Row Changes","description":"Feature availability — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-10-029?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_riNi4ab-RUID8faGY_Imy","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 by performing string normalisation and detecting adjacent row changes across up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and adjacent row change analysis on my feature availability data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04 with populated_features values like 10, 20, 15, 30 and expected_features of 1, 2, 3, 4 — analyze the populated_features field.","exampleUseCases":[{"title":"Feature completeness audit by dataset group","prompt":"I have 50 rows of feature availability records across two dataset groups — 'train-set' and 'test-set' — with dates from 2025-01-01 to 2025-02-19. Can you run string normalisation and detect adjacent row changes on the populated_features field to help me understand where feature counts shift day-over-day?"},{"title":"Expected vs populated feature gap detection","prompt":"My ML pipeline tracks expected_features and populated_features for each row daily. Can you analyze these 120 rows of availability data and generate both the string normalisation and adjacent row change reports on expected_features so I can see where things deviate?"},{"title":"Data quality check before model training","prompt":"Before I kick off model training I want to sanity-check my feature availability table — I've got 200 rows with dataset_group, date, populated_features, and expected_features columns. Run the string normalisation and adjacent row changes analysis on populated_features and tell me what you find."}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report standardizing the feature availability values across submitted rows, and (2) an adjacent row changes report identifying where consecutive rows differ in the selected feature field (populated_features or expected_features). Results cover up to 200 submitted rows with no external data sources.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Null values in both populated_features and expected_features — may produce incomplete report","Invalid field enum value (must be 'populated_features' or 'expected_features') — error","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you need fast, local (no external lookup) analytical computation on feature availability records — specifically when you need both string normalisation and adjacent row change detection in a single call for up to 200 rows. Ideal for ML pipeline data quality checks, feature store audits, and dataset completeness monitoring where you want two distinct reports from one low-cost API call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:51:40.292Z","isFirstParty":false,"canonicalSlug":"feature-availability-string-normalisation-adjacent-row-changes-f2a17721"}