{"uid":"cap_8-ReBTnZCgeFLfXkAZndY","slug":"data-ingestion-batches-adjacent-row-changes-iqr-outlier-flags-1f9ee9d7","name":"Data Ingestion Batches — Adjacent Row Changes + IQR Outlier Flags","description":"Data ingestion batches — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied data ingestion batches records (received_rows, accepted_rows). 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-01-157?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"}}},"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_ayCcboDTPEXALpM9ZKVy1","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 up to 200 data ingestion batch records to produce two local analytical reports: adjacent row-to-row change detection and IQR-based outlier flagging for received_rows or accepted_rows fields.","exampleAgentPrompt":"Can you check my ingestion batch data for outliers and adjacent row changes? Here are my records across two groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04 and their received_rows counts. I want the IQR outlier flags and day-over-day change report for received_rows.","exampleUseCases":[{"title":"Pipeline drop detection for daily ingestion","prompt":"I have daily ingestion batch records for the last 30 days with received_rows and accepted_rows values per dataset group. Can you run the adjacent row change and IQR outlier detection on received_rows to find any suspicious drops or spikes?"},{"title":"Acceptance rate anomaly audit","prompt":"Here are my ingestion batch rows for dataset groups alpha and beta over the past two weeks. Run both the adjacent row change and IQR outlier reports on accepted_rows so I can see which days are statistically unusual."},{"title":"Multi-group ingestion quality check","prompt":"I want to audit ingestion consistency across three dataset groups — prod, staging, and dev — using about 150 batch records. Can you flag IQR outliers and show me row-to-row changes for received_rows?"}],"resultDescription":"Returns two analytical reports: (1) adjacent row change analysis showing delta values between consecutive rows for the selected field (received_rows or accepted_rows), and (2) IQR outlier flags identifying which rows fall outside the interquartile range bounds — all computed locally with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows submitted — exceeds maxItems limit","Invalid date format (non YYYY-MM-DD) — schema validation error","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — rejected","Field enum value not 'received_rows' or 'accepted_rows' — schema validation error","Numeric values outside allowed range (±1,000,000,000,000) — rejected"],"whenToPreferThis":"Choose this endpoint when you have up to 200 data ingestion batch records and need both adjacent row-change detection and IQR outlier flagging in a single low-cost call. It is ideal for automated data quality pipelines, ingestion audits, and anomaly triage on received_rows or accepted_rows metrics. It does not perform external lookups, ML predictions, or live data fetching — prefer it for offline or batch analytical passes on already-collected ingestion logs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T00:52:25.709Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-adjacent-row-changes-iqr-outlier-flags-1f9ee9d7"}