{"uid":"cap_dwLhzWx18ERXZXg3Ks-0d","slug":"sampling-observations-analytics-record-quality-audit-string-3bf17756","name":"Sampling Observations Analytics: Record Quality Audit + String Normalisation + Keyed Dataset Comparison","description":"Sampling observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied sampling observations records (sample_value, sample_weight). 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 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three local analytical reports on sampling observation records (sample_value, sample_weight): a record quality audit, string normalisation, and keyed dataset comparison between two supplied datasets.","exampleAgentPrompt":"Can you run the three-report analytics bundle on my sampling observations — record quality audit, string normalisation, and keyed dataset comparison — for these two datasets of up to 200 rows each with fields row_id, dataset_group, date, sample_value, and sample_weight?","exampleUseCases":[{"title":"Survey sample data quality check","prompt":"I've collected 150 survey sampling records with sample_value and sample_weight fields and I want to audit them for quality issues, normalise any inconsistent strings in dataset_group, and compare them against last month's dataset to spot differences — can you run all three reports at once?"},{"title":"A/B test weighted sample comparison","prompt":"I have two groups of sampling observations from an A/B test, each with row_id, date, sample_value, and sample_weight — please audit the data quality, normalise the group labels, and compare the two datasets by key to highlight any mismatches."},{"title":"Environmental monitoring data validation","prompt":"Here are two batches of environmental sampling records (up to 200 rows each) with date, sample_value, and sample_weight — run the record quality audit, string normalisation, and keyed comparison so I can see where the datasets diverge and whether there are any data issues."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging issues like missing values, out-of-range numbers, or malformed dates; (2) a string normalisation report standardising text fields such as dataset_group; and (3) a keyed dataset comparison report highlighting differences between the primary and secondary supplied datasets — all computed locally with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors","Invalid date format (must be YYYY-MM-DD) causes schema rejection","sample_value or sample_weight outside allowed range (-1e12 to 1e12) causes validation error","Empty rows array causes error","Payment of 0.01 USDC not provided via x402 protocol results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously validate record quality, normalise strings, and compare two keyed sampling observation datasets in a single cheap API call (0.01 USDC). It is ideal for batch data quality workflows on datasets up to 200 rows where no live lookups, predictions, or external data are needed — all analysis is performed locally on the supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:14.646Z","isFirstParty":false,"canonicalSlug":"sampling-observations-analytics-record-quality-audit-string-3bf17756"}