{"uid":"cap_5T9rq8OaQNY5eYgKmcX7t","slug":"compositing-graphs-category-frequencies-iqr-outlier-flags-c1d37975","name":"Compositing Graphs — Category Frequencies + IQR Outlier Flags","description":"Compositing graphs — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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-09-107?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_oY5wB8hQ0rgXjoIXn0lVW","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":"Analyzes compositing graph records to produce category frequency distributions and IQR-based outlier flags for node_count or render_seconds fields, across up to 200 rows.","exampleAgentPrompt":"I have a list of compositing graph records with asset groups, dates, node counts, and render times — can you analyze them for category frequencies and flag any IQR outliers in the node_count field? Here's the data: [{\"row_id\":\"r1\",\"asset_group\":\"bg-layer\",\"date\":\"2026-10-01\",\"node_count\":45,\"render_seconds\":12}, {\"row_id\":\"r2\",\"asset_group\":\"fx-layer\",\"date\":\"2026-10-02\",\"node_count\":300,\"render_seconds\":8}].","exampleUseCases":[{"title":"Detecting render time anomalies in pipeline","prompt":"I have 80 compositing graph records from our rendering pipeline — can you run IQR outlier detection on the render_seconds field and also tell me how often each asset_group appears? I want to know which jobs are statistical outliers."},{"title":"Auditing node count spikes by asset group","prompt":"Here are 150 rows of compositing graph data with node counts and asset groups. Can you compute the category frequencies for asset_group and flag any IQR outliers in node_count so I can see which scenes are abnormally complex?"},{"title":"QA check on weekly compositing batch","prompt":"We just ran a weekly compositing batch and I have the results as a table of rows with row_id, asset_group, date, node_count, and render_seconds. Please give me the category frequency breakdown and IQR outlier flags for render_seconds so we can QA the output."}],"resultDescription":"Two statistical reports: (1) a category frequency distribution showing how often each asset_group value appears in the supplied rows, and (2) IQR-based outlier flags for the selected numeric field (node_count or render_seconds), identifying which rows fall outside the interquartile range threshold. Results are computed locally on the supplied data with no external lookups.","failureModes":["Empty or missing rows array returns an error","More than 200 rows causes rejection due to maxItems limit","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Missing required fields (row_id, asset_group, date, node_count, render_seconds) causes row rejection","Invalid field enum value (not node_count or render_seconds) returns error","Numeric values outside ±1 trillion range are rejected"],"whenToPreferThis":"Choose this endpoint when you have compositing graph records (with asset_group, node_count, and render_seconds fields) and need both a categorical frequency summary and IQR-based outlier detection in a single low-cost call. Ideal for pipeline QA, batch job auditing, or anomaly detection on rendering data without requiring an external analytics platform.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T12:43:17.227Z","isFirstParty":false,"canonicalSlug":"compositing-graphs-category-frequencies-iqr-outlier-flags-c1d37975"}