{"uid":"cap_PACP07WuKPkN6cEKeK1iq","slug":"schema-conformity-daily-sum-series-iqr-outlier-flags-1ffdd092","name":"Schema Conformity — Daily Sum Series + IQR Outlier Flags","description":"Schema conformity — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags for supplied schema conformity records (valid_records, checked_records). 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-02-140?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","valid_records","checked_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"valid_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"checked_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","valid_records":10,"checked_records":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","valid_records":20,"checked_records":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","valid_records":15,"checked_records":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","valid_records":30,"checked_records":4}],"maxItems":200,"minItems":1},"field":{"enum":["valid_records","checked_records"],"type":"string","default":"valid_records"}}},"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_t5lAWQ8ncBDu9UViMn3fm","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 schema conformity records (valid_records and checked_records) over up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Analyze my schema conformity logs — I have up to 100 rows with columns row_id, dataset_group, date, valid_records, and checked_records — and give me both a daily sum time series and IQR outlier flags for the valid_records field.","exampleUseCases":[{"title":"Daily data quality monitoring pipeline","prompt":"I have daily schema conformity records for two dataset groups from last quarter — each row has a date, valid_records, and checked_records. Can you compute the daily sum series and flag any IQR outliers in the valid_records field so I can spot which days had abnormal validation counts?"},{"title":"Detecting spikes in checked records","prompt":"Run an IQR outlier analysis on my checked_records data across these 150 rows of schema conformity logs — I want to see the daily totals and which dates are statistical outliers based on interquartile range."},{"title":"Cross-group schema conformity audit","prompt":"I'm auditing conformity across two dataset groups and need a daily sum series plus outlier flags for valid_records. Here are 80 rows with row_id, date, dataset_group, valid_records, and checked_records — can you produce both reports?"}],"resultDescription":"Returns two independent reports: (1) a daily sum time series aggregating the chosen field (valid_records or checked_records) by date, and (2) IQR outlier flags per row indicating which observations fall outside the interquartile range. Both reports are computed locally from supplied data with no external lookups.","failureModes":["Missing required fields (row_id, date, valid_records, checked_records) causes validation error","More than 200 rows submitted exceeds the maxItems limit and will be rejected","Invalid date format (not YYYY-MM-DD) causes schema rejection","null values in numeric fields are accepted but may affect aggregation results","Submitting fewer than 1 row returns an error due to minItems constraint"],"whenToPreferThis":"Choose this endpoint when you need both a daily aggregated time series AND statistical outlier detection (IQR-based) for schema conformity metrics in a single low-cost call. It is purpose-built for valid_records and checked_records fields, making it ideal for data quality pipelines monitoring record validation rates over time. Prefer it over generic analytics tools when your data is already in the required row format and you want two bundled reports for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:19.678Z","isFirstParty":false,"canonicalSlug":"schema-conformity-daily-sum-series-iqr-outlier-flags-1ffdd092"}