{"uid":"cap_vAaX4lO_vlxtU7xEvr8RL","slug":"sampling-observations-iqr-outlier-flags-keyed-dataset-comparison-12a5c3d5","name":"Sampling Observations IQR Outlier Flags + Keyed Dataset Comparison","description":"Sampling observations — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison for supplied sampling observations records (sample_value, sample_weight). 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 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two local analytical reports on sampling observation records: IQR-based outlier flagging and keyed dataset comparison, for up to 200 rows at $0.01 USDC.","exampleAgentPrompt":"I have two sets of sampling observation records (up to 200 rows each, with row_id, dataset_group, date, sample_value, and sample_weight) — can you run IQR outlier flagging on the sample_value field and also compare the two datasets row-by-row to spot differences?","exampleUseCases":[{"title":"Outlier detection in environmental samples","prompt":"I've got 150 rows of water quality sampling data with sample_value as the measured concentration and sample_weight as site volume — can you flag which rows are IQR outliers on sample_value and compare today's dataset against last week's baseline to see what changed?"},{"title":"QC check on survey response data","prompt":"Run IQR outlier detection on my survey response scores (sample_value) and compare my current batch of 80 responses against the prior wave — I need to know which respondents look anomalous and where the two datasets diverge by row_id."},{"title":"Manufacturing batch sample validation","prompt":"I have two batches of production measurement readings — 120 rows each with batch ID as dataset_group, measurement date, sample_value as the reading, and sample_weight as unit count — please flag any IQR outliers in sample_value and show me the keyed comparison between the two batches."}],"resultDescription":"Returns two analytical reports: (1) per-row IQR outlier flags indicating which rows fall outside the interquartile range for the selected field (sample_value or sample_weight), and (2) a keyed dataset comparison report showing row-by-row differences between the primary and secondary datasets matched on row_id and dataset_group. No external lookups or predictions are made; all analysis is performed locally on the supplied data.","failureModes":["Exceeding 200-row limit returns a validation error","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes schema rejection","Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure","Null values in sample_value or sample_weight may reduce outlier detection accuracy","Mismatched row_ids between primary and secondary datasets result in unmatched rows in the comparison report","Payment failure via x402 protocol prevents processing"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection AND a keyed row-by-row dataset comparison on sampling observation data in a single low-cost call ($0.01 USDC for both reports). Ideal for data quality workflows where you want to validate a new batch of up to 200 sampling records against a reference dataset while simultaneously flagging statistical anomalies. Prefer this over general-purpose analytics APIs when working with structured sample_value/sample_weight records and needing reproducible, offline analytical results without external data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:18.397Z","isFirstParty":false,"canonicalSlug":"sampling-observations-iqr-outlier-flags-keyed-dataset-comparison-12a5c3d5"}