{"uid":"cap_xlc3LBrinfNhciTxwmxhv","slug":"dataset-partition-sizes-exact-row-deduplication-iqr-outlier-flags-a5af53fe","name":"Dataset Partition Sizes — Exact Row Deduplication + IQR Outlier Flags","description":"Dataset partition sizes — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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-07-049?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_4ezkos3K3qLSOT6ThpqAX","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":"Deduplicates dataset partition size records and flags IQR-based outliers across row_count and file_mb fields for up to 200 rows in two independent analytical reports.","exampleAgentPrompt":"Can you deduplicate and flag IQR outliers for my dataset partition records? I have rows with row_id, dataset_group, date, row_count, and file_mb — here are up to 200 entries — and I want the analysis run on the row_count field.","exampleUseCases":[{"title":"Partition size anomaly detection for data pipeline","prompt":"I have a list of daily dataset partition snapshots with row counts and file sizes. Can you deduplicate the records and flag any partitions whose row_count looks like a statistical outlier using IQR? Here are my 80 rows with row_id, dataset_group, date, row_count, and file_mb."},{"title":"Storage audit cleanup across dataset groups","prompt":"We're auditing file sizes across our dataset groups and I need to remove exact duplicate records first, then identify which entries have unusually high or low file_mb values based on IQR. Can you run both reports on these 150 partition records, analyzing the file_mb field?"},{"title":"Data quality check before ingestion","prompt":"Before I ingest these partition size records into our warehouse, can you deduplicate them and highlight any row_count outliers using interquartile range analysis? I've got about 120 rows each with a row_id, group name, date, row_count, and file_mb."}],"resultDescription":"Returns two independent analytical reports per call: (1) an exact row deduplication report identifying and removing duplicate records from the supplied dataset partition rows, and (2) an IQR-based outlier flagging report marking records whose selected metric (row_count or file_mb) falls outside the interquartile fence. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — exceeds maxItems limit, request rejected","Missing required fields (row_id, dataset_group, date, row_count, file_mb) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema validation failure","Empty rows array (minItems: 1 violated) — request rejected","Numeric values outside allowed range (±1,000,000,000,000) — schema validation failure","Invalid field selector (not 'row_count' or 'file_mb') — enum validation error"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost ($0.01 USDC) combined deduplication and IQR outlier detection pass on dataset partition metrics (row_count or file_mb) for batches up to 200 rows. It is ideal for data engineering pipelines that need automated data quality checks before ingestion, without requiring an external analytics platform or live data connection. Prefer alternatives if you need more than 200 rows, require prediction or forecasting, need professional statistical assessment, or require analysis beyond row_count and file_mb fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:08:08.339Z","isFirstParty":false,"canonicalSlug":"dataset-partition-sizes-exact-row-deduplication-iqr-outlier-flags-a5af53fe"}