{"uid":"cap_tDyEoWEf4rR_nJlsuiWLd","slug":"dataset-storage-record-quality-audit-string-normalisation-numeric-3688b032","name":"Dataset Storage Record Quality Audit, String Normalisation & Numeric Interval Filter","description":"Dataset storage — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied dataset storage records (used_mb, row_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-197?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","used_mb","row_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"used_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","used_mb":10,"row_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","used_mb":20,"row_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","used_mb":15,"row_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","used_mb":30,"row_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["used_mb","row_count"],"type":"string","default":"used_mb"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_44-AAolrpKrI1eophe_O0","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 three local analytical reports on dataset storage records: a record quality audit, string normalisation, and a numeric interval filter for used_mb or row_count fields.","exampleAgentPrompt":"Can you audit these dataset storage records for quality issues, normalise the dataset_group strings, and filter rows where used_mb is between 0 and 100? I've got up to 50 rows with fields: row_id, dataset_group, date, used_mb, and row_count.","exampleUseCases":[{"title":"Pre-ingestion storage data validation","prompt":"Before I load these dataset storage records into the warehouse, can you run a quality audit on them and flag any rows with nulls, bad dates, or unexpected values in used_mb or row_count?"},{"title":"Filtering storage records by size range","prompt":"I have a list of dataset storage rows and I only want the ones where used_mb falls between 50 and 500 — can you filter them for me and also tell me which ones have normalisation issues in the dataset_group field?"},{"title":"Cleaning group names in storage exports","prompt":"These storage records have messy dataset_group values — can you normalise the strings and also give me a quality report so I know which rows are clean enough to trust?"}],"resultDescription":"Returns three independent reports: (1) a record quality audit flagging nulls, format violations, and anomalies; (2) a string normalisation report for the dataset_group field; (3) a numeric interval filter report identifying rows where the selected field (used_mb or row_count) falls within or outside the specified minimum/maximum range. All analysis is performed locally with no external lookups. Up to 200 rows per call.","failureModes":["Fewer than 1 row submitted returns a validation error","More than 200 rows submitted exceeds the row limit and will be rejected","Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause schema validation failure","Dates not matching YYYY-MM-DD pattern cause parsing errors","Minimum greater than maximum in interval filter may produce empty or error result","Non-numeric values in used_mb or row_count fields cause type errors"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) batch quality check on dataset storage records — specifically when you want all three of quality auditing, string normalisation, and numeric range filtering in a single call without any external API dependencies, live data lookups, or ML predictions. Ideal for pre-ingestion validation pipelines on small-to-medium batches (up to 200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:07.535Z","isFirstParty":false,"canonicalSlug":"dataset-storage-record-quality-audit-string-normalisation-numeric-3688b032"}