{"uid":"cap_baRsm1ySf5Lr6LfiSfH0s","slug":"file-ingestion-iqr-outlier-flags-11747442","name":"File Ingestion IQR Outlier Flags","description":"File ingestion - IQR outlier flags: IQR outlier flags for File ingestion. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-file-ingestion-outliers?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","source","date","loaded_bytes","parse_errors"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source":{"type":"string","default":"","maxLength":80},"loaded_bytes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"parse_errors":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source":"group-a","loaded_bytes":10,"parse_errors":1},{"date":"2026-10-02","row_id":"row-2","source":"group-b","loaded_bytes":20,"parse_errors":2},{"date":"2026-10-03","row_id":"row-3","source":"group-a","loaded_bytes":15,"parse_errors":3},{"date":"2026-10-04","row_id":"row-4","source":"group-b","loaded_bytes":30,"parse_errors":4}],"maxItems":200,"minItems":1},"field":{"enum":["loaded_bytes","parse_errors"],"type":"string","default":"loaded_bytes"}}},"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_AW3lhfah3GBBr99qcrQAl","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":"Computes IQR-based outlier flags on file ingestion metrics (loaded_bytes or parse_errors) for up to 200 typed records","exampleAgentPrompt":"Can you run IQR outlier detection on my file ingestion records and flag which rows have abnormal loaded_bytes values? I have 4 records: row-1 from group-a on 2026-10-01 with 10 bytes and 1 parse error, row-2 from group-b on 2026-10-02 with 20 bytes and 2 parse errors, row-3 from group-a on 2026-10-03 with 15 bytes and 3 parse errors, and row-4 from group-b on 2026-10-04 with 30 bytes and 4 parse errors.","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have daily file ingestion logs from two sources and I want to know which rows have abnormally high or low loaded_bytes — run IQR outlier flagging on these 10 records and tell me which ones are outliers."},{"title":"Parse error spike identification","prompt":"Can you check my ingestion records for the past week and flag any rows where parse_errors look like statistical outliers using the IQR method? I want to know which jobs are behaving abnormally."},{"title":"Data quality audit on ingestion metrics","prompt":"We're auditing our file ingestion pipeline and need to flag outlier rows in loaded_bytes across about 50 records from multiple sources — can you run IQR outlier detection and give me the flagged rows?"}],"resultDescription":"A bounded IQR outlier flags report identifying which of the submitted file ingestion records have statistically anomalous values for the chosen field (loaded_bytes or parse_errors), based on interquartile range analysis. No enrichment or forecasting is included — purely local descriptive analytics.","failureModes":["Empty rows array or missing required fields (row_id, source, date, loaded_bytes, parse_errors) returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Non-numeric values for loaded_bytes or parse_errors where numbers are expected may be rejected","Payment failure via x402 protocol prevents endpoint access"],"whenToPreferThis":"Choose this endpoint when you need fast, local IQR-based outlier detection specifically on file ingestion metrics (loaded_bytes or parse_errors) without requiring external data enrichment or forecasting. It is ideal for data engineers auditing ETL pipelines, spotting anomalous ingestion jobs, or performing lightweight descriptive analytics on up to 200 records at a very low cost ($0.01 USDC per call).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T00:59:09.821Z","isFirstParty":false,"canonicalSlug":"file-ingestion-iqr-outlier-flags-11747442"}