{"uid":"cap_GDBlY8WvNHwGpUoN4KZhu","slug":"frame-timing-record-quality-audit-string-normalisation-secondary-22940621","name":"Frame Timing Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean","description":"Frame timing — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied frame timing records (frame_ms, draw_calls). 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 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-game-01-205?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","frame_ms","draw_calls"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"frame_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"game_area":{"type":"string","default":"","maxLength":80},"draw_calls":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","frame_ms":10,"game_area":"group-a","draw_calls":1},{"date":"2026-10-02","row_id":"row-2","frame_ms":20,"game_area":"group-b","draw_calls":2},{"date":"2026-10-03","row_id":"row-3","frame_ms":15,"game_area":"group-a","draw_calls":3},{"date":"2026-10-04","row_id":"row-4","frame_ms":30,"game_area":"group-b","draw_calls":4}],"maxItems":200,"minItems":1},"field":{"enum":["frame_ms","draw_calls"],"type":"string","default":"frame_ms"}}},"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_2efT5ADApkUuB_KPnuj_A","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":"Audits quality, normalises strings, and computes a secondary-measure weighted mean for up to 200 frame timing records (frame_ms, draw_calls) in three independent analytical reports.","exampleAgentPrompt":"Can you run all three analytical reports on my frame timing data — record quality audit, string normalisation, and secondary-measure weighted mean — for these rows: row-1 on 2026-10-01 in group-a with frame_ms=10 and draw_calls=1, row-2 on 2026-10-02 in group-b with frame_ms=20 and draw_calls=2? Use frame_ms as the primary field.","exampleUseCases":[{"title":"Game area string cleanup and quality check","prompt":"I have 150 frame timing records from our game build with messy game_area labels and some null values in frame_ms — can you run a record quality audit and string normalisation on all of them so I can see what's dirty before I import it into our database?"},{"title":"Weighted mean frame time by draw call volume","prompt":"I want to compute a draw_calls-weighted average of frame_ms across my 80 frame timing rows — can you run the secondary-measure weighted mean report on this data and give me the result?"},{"title":"Full three-report pipeline for performance regression","prompt":"We just finished a performance regression test and I have 200 rows of frame timing records with game_area, date, frame_ms, and draw_calls — please run all three reports: quality audit, string normalisation, and weighted mean using frame_ms, so I can identify bad records and get a clean aggregate summary."}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit flagging missing, out-of-range, or malformed values in the supplied frame timing rows; (2) string normalisation results for text fields such as game_area; and (3) a secondary-measure weighted mean of the chosen primary field (frame_ms or draw_calls) weighted by the other measure — all computed locally with no external lookups.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, game_area, date, frame_ms, draw_calls) in any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","field enum value not 'frame_ms' or 'draw_calls' — rejected","All rows have null values for chosen field — weighted mean cannot be computed","Numeric values outside allowed range — clamped or flagged in audit report"],"whenToPreferThis":"Choose this endpoint when you need three combined analytical reports — quality audit, string normalisation, and weighted mean — specifically for frame timing game performance data (frame_ms and draw_calls fields) in a single $0.01 call covering up to 200 rows. Prefer it over general analytics APIs when you want a bundled, low-cost batch analysis with no external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:31:58.008Z","isFirstParty":false,"canonicalSlug":"frame-timing-record-quality-audit-string-normalisation-secondary-22940621"}