{"uid":"cap_mzA_N33nenxADKMi4Rzln","slug":"numeric-precision-comparisons-daily-sum-series-iqr-outlier-flags-384bf058","name":"Numeric Precision Comparisons — Daily Sum Series + IQR Outlier Flags","description":"Numeric precision comparisons — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-140?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"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_yafkapP5B51R2uGvK54T3","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 daily sum time series and IQR-based outlier flags for supplied source_value/transformed_value numeric comparison records (up to 200 rows, 2 reports per call).","exampleAgentPrompt":"I have a batch of up to 200 rows with date, source_value, and transformed_value fields — can you run the daily sum series and IQR outlier flagging analysis on the source_value field for me? Here are the rows: [{\"row_id\":\"r1\",\"dataset_group\":\"sales\",\"date\":\"2024-01-01\",\"source_value\":150,\"transformed_value\":148},{\"row_id\":\"r2\",\"dataset_group\":\"sales\",\"date\":\"2024-01-01\",\"source_value\":9000,\"transformed_value\":8950}].","exampleUseCases":[{"title":"ETL pipeline data quality audit","prompt":"I just ran an ETL transform on our sales figures and want to check if any rows have suspicious divergence. Can you analyze these 120 records — each has a date, source_value, and transformed_value — and give me the daily sum series plus flag any IQR outliers in the source_value field?"},{"title":"Financial reconciliation anomaly detection","prompt":"We're reconciling daily transaction totals across two systems. Here are 180 rows with the original and adjusted amounts by date — please compute the daily sums and highlight any transformed_value entries that fall outside the IQR as outliers."},{"title":"Sensor reading precision validation","prompt":"I have 95 sensor readings paired with their calibrated values, each tagged with a date and a dataset_group. Run the IQR outlier detection on the transformed_value column and give me the daily aggregated sum series so I can spot drift patterns."}],"resultDescription":"Returns two analytical reports: (1) a daily sum series aggregating the selected numeric field (source_value or transformed_value) by date, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range. All computation is performed locally on the supplied data — no external lookups or predictions are made.","failureModes":["Fewer than 1 or more than 200 rows provided — request rejected","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) — validation error","Date strings not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside ±1,000,000,000,000 range — rejected","Payment not processed (x402 protocol failure) — no result returned","All values null for selected field — IQR computation may return no meaningful outlier flags"],"whenToPreferThis":"Choose this endpoint when you need to (a) detect statistical outliers via IQR in a batch of up to 200 paired numeric records (source vs. transformed), and (b) simultaneously get daily sum aggregations — all in one $0.01 USDC call with no external data dependencies. Prefer it over general-purpose analytics APIs when your use case is specifically comparing source and transformed numeric precision across dates and you want both time-series aggregation and outlier detection in a single lightweight, local-computation call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:41.440Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-daily-sum-series-iqr-outlier-flags-384bf058"}