{"uid":"cap_3iALtnKAjd_A3jn68JVAe","slug":"dataset-partition-sizes-record-quality-audit-string-normalisation-iqr-a04f48be","name":"Dataset Partition Sizes — Record Quality Audit, String Normalisation & IQR Outlier Flags","description":"Dataset partition sizes — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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-07-203?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","row_count","file_mb"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"file_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","file_mb":1,"row_count":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","file_mb":2,"row_count":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","file_mb":3,"row_count":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","file_mb":4,"row_count":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["row_count","file_mb"],"type":"string","default":"row_count"}}},"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_i27iJlTVW8PxoPWaPXG4j","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 record quality, normalises strings, and flags IQR-based outliers for dataset partition size records (row_count, file_mb) — delivering 3 analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and IQR outlier check on my dataset partition records? I have 4 rows with row_id, dataset_group, date, row_count, and file_mb values — I want to analyse the row_count field specifically.","exampleUseCases":[{"title":"ETL pipeline partition audit","prompt":"I've got a list of dataset partitions from last week's ETL runs — each has a row_id, dataset_group, date, row_count, and file_mb. Can you run a quality audit, normalise any messy group name strings, and flag any IQR outliers in the row_count column?"},{"title":"Detecting oversized file partitions","prompt":"Here are 50 partition records from our data lake, each with row counts and file sizes in MB. Can you check them for record quality issues, clean up the dataset_group labels, and highlight any file_mb values that look like outliers using IQR?"},{"title":"Data pipeline health check for QA review","prompt":"Before our QA review, I need a health check on these 120 dataset partition records — run the record quality audit, string normalisation, and IQR outlier flags on the file_mb field so I can spot anything suspicious."}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit identifying missing, malformed, or invalid fields; (2) string normalisation findings for text fields like dataset_group; and (3) IQR-based outlier flags on the chosen numeric field (row_count or file_mb). All reports are computed locally with no external lookups.","failureModes":["Input array is empty or exceeds 200 rows — rejected with validation error","Required fields (row_id, date, row_count, file_mb, dataset_group) missing from row objects — schema validation failure","Date fields not matching YYYY-MM-DD pattern — rejected","Numeric fields outside allowed range — rejected","Payment of 0.01 USDC not completed — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need a combined data quality pass — record auditing, text normalisation, and statistical outlier detection — on dataset partition metadata (row counts and file sizes) in a single $0.01 call. It is purely analytical with no side effects, making it safe to call repeatedly. Prefer it over general-purpose data quality tools when your records specifically contain row_count and file_mb fields and you want IQR-based outlier flagging alongside string cleanup in one bundled response.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:33.857Z","isFirstParty":false,"canonicalSlug":"dataset-partition-sizes-record-quality-audit-string-normalisation-iqr-a04f48be"}