{"uid":"cap_7sRHJSuiMc3RX1wTOmFD1","slug":"feature-availability-record-quality-audit-string-normalisation-c2e6825e","name":"Feature Availability — Record Quality Audit + String Normalisation","description":"Feature availability — Record quality audit + String normalisation: Record quality audit + String normalisation 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-10-001?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}}},"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_nnRbOZuRo-2o9F7bm_DZb","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":"Audits data quality and normalises strings for up to 200 feature availability records, returning two independent analytical reports with no external lookups.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on my feature availability data? I have 4 rows — row-1 for group-a on 2026-10-01 with 10 populated and 1 expected, row-2 for group-b on 2026-10-02 with 20 populated and 2 expected, row-3 for group-a on 2026-10-03 with 15 populated and 3 expected, and row-4 for group-b on 2026-10-04 with 30 populated and 4 expected.","exampleUseCases":[{"title":"ML feature store completeness check","prompt":"I need to audit my ML feature store — can you run a record quality audit and string normalisation on these 50 feature availability records from last week, grouped by dataset_group, and tell me which rows have quality issues or malformed strings?"},{"title":"Daily feature pipeline data quality report","prompt":"Run a quality audit and string normalisation report on today's feature availability batch — I have 120 rows from groups alpha, beta, and gamma dated 2025-07-15, with populated_features and expected_features counts for each row. Flag anything that looks off."},{"title":"Pre-training data validation for feature completeness","prompt":"Before I kick off model training, can you validate these 80 feature availability records — row IDs ft-001 through ft-080, all from 2025-06-30 across three dataset groups — and give me a quality audit plus normalised strings so I can catch any issues?"}],"resultDescription":"Two independent analytical reports: (1) a record quality audit highlighting data issues across the supplied feature availability rows, and (2) a string normalisation report cleaning and standardising string fields. Both reports are generated locally with no external lookups, covering up to 200 rows in a single call.","failureModes":["Exceeds 200-row limit — request rejected","Missing required fields (row_id, date, populated_features, expected_features) — validation error","Date not in YYYY-MM-DD format — pattern validation failure","populated_features or expected_features outside allowed numeric range — schema rejection","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you need both a record quality audit and string normalisation on feature availability data in a single low-cost call (0.01 USDC for both reports). Ideal for ML pipelines validating feature completeness before training, data engineering teams auditing feature stores, or anyone needing offline analytical checks on populated vs expected feature counts without any live lookups or external API dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:34.538Z","isFirstParty":false,"canonicalSlug":"feature-availability-record-quality-audit-string-normalisation-c2e6825e"}