{"uid":"cap_yu7RRTmWWoiQghPNAL2Ia","slug":"compositing-graphs-iqr-outlier-flags-and-two-measure-correlation-6d176e70","name":"Compositing Graphs IQR Outlier Flags and Two-Measure Correlation Analysis","description":"Compositing graphs — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation for supplied compositing graphs records (node_count, render_seconds). 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-creative-09-176?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","node_count","render_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"node_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","node_count":10,"asset_group":"group-a","render_seconds":1},{"date":"2026-10-02","row_id":"row-2","node_count":20,"asset_group":"group-b","render_seconds":2},{"date":"2026-10-03","row_id":"row-3","node_count":15,"asset_group":"group-a","render_seconds":3},{"date":"2026-10-04","row_id":"row-4","node_count":30,"asset_group":"group-b","render_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["node_count","render_seconds"],"type":"string","default":"node_count"}}},"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_Ewo6x5-6wPwfcJWKk8iXy","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":"Runs IQR-based outlier detection and two-measure correlation analysis on user-supplied compositing graph records (node_count, render_seconds) for up to 200 rows.","exampleAgentPrompt":"Can you run IQR outlier detection and a two-measure correlation between node_count and render_seconds on my compositing graph data? I have about 50 rows with fields for row_id, asset_group, date, node_count, and render_seconds — I want to know which rows are outliers and how strongly the two metrics are correlated.","exampleUseCases":[{"title":"Render pipeline anomaly detection","prompt":"I have a batch of compositing graph records from last week — each row has a row_id, asset_group, date, node_count, and render_seconds. Can you flag any rows where node_count looks like an IQR outlier and also tell me if node_count and render_seconds are correlated?"},{"title":"Asset group performance audit","prompt":"Here are 120 rows of compositing graph data across multiple asset groups, each with node_count and render_seconds. Run IQR outlier flagging on node_count and give me the correlation between node_count and render_seconds so I can see if heavier graphs actually take longer to render."},{"title":"Spot-check compositing job metrics","prompt":"I want to quality-check my compositing pipeline data — I have about 80 records with row_id, asset_group, date, node_count, and render_seconds. Flag any IQR outliers in the node_count field and show me how strongly node_count and render_seconds move together."}],"resultDescription":"Returns two independent analytical reports: (1) IQR outlier flags identifying which rows have node_count or render_seconds values outside the interquartile range bounds, and (2) a two-measure correlation report quantifying the statistical relationship between node_count and render_seconds across the supplied rows.","failureModes":["Fewer than 1 row supplied — validation error","More than 200 rows supplied — request rejected","Missing required fields (row_id, date, node_count, render_seconds) — schema validation failure","Non-date-formatted date string — pattern validation error","Null values in both measures — insufficient data for correlation","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have compositing graph pipeline data with node_count and render_seconds fields and need both outlier detection and correlation analysis in a single call. It is purpose-built for this schema (asset_group, date, node_count, render_seconds) and returns two bundled reports for a flat $0.01 USDC, making it more cost-effective than calling separate statistical services. Best when you already have the data locally and need fast, offline-style analytics without any external data lookup.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:11.210Z","isFirstParty":false,"canonicalSlug":"compositing-graphs-iqr-outlier-flags-and-two-measure-correlation-6d176e70"}