{"uid":"cap_NxWg_5MNQGDE_16UYp9Jw","slug":"frame-timing-analyzer-record-quality-audit-string-normalisation-b5e0d8e5","name":"Frame Timing Analyzer — Record Quality Audit + String Normalisation + Numeric Row Ordering","description":"Frame timing — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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-01-196?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_D2JmyMrET6Ri14lr4gBBe","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 sorts rows for supplied frame timing records (frame_ms, draw_calls) in up to 200 rows, producing 3 independent analytical reports.","exampleAgentPrompt":"Can you audit the quality, normalise the game_area strings, and sort these frame timing rows numerically by frame_ms? I have records with row IDs, dates, game areas like 'group-a' and 'group-b', and frame_ms and draw_calls values — about 50 rows total.","exampleUseCases":[{"title":"Game area data quality check","prompt":"I've got 80 rows of frame timing data from our game — each has a row_id, date, game_area, frame_ms, and draw_calls. Can you run a quality audit, normalise the game_area strings, and order the rows numerically by frame_ms so I can spot the worst-performing frames?"},{"title":"Draw call outlier ordering","prompt":"Here are 150 frame timing records from our rendering pipeline — can you audit their data quality, clean up the game_area strings, and sort them numerically by draw_calls so we can identify which scenes have the highest draw call counts?"},{"title":"Pre-release performance data cleanup","prompt":"Before our studio review, I need these 60 frame timing rows cleaned up — please run a record quality audit, normalise the game_area labels, and sort everything by frame_ms in numeric order."}],"resultDescription":"Three independent reports per call: (1) a record quality audit flagging missing, malformed, or out-of-range values in the supplied rows; (2) a string normalisation report for the game_area field; and (3) a numeric row ordering report sorting rows by the selected field (frame_ms or draw_calls). All analysis is performed locally on the submitted data with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, game_area, date, frame_ms, draw_calls) causes row-level rejection","Invalid date format (not YYYY-MM-DD) triggers a schema validation error","Non-numeric values outside the allowed range for frame_ms or draw_calls are flagged or rejected","Empty rows array returns an error due to minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you have raw game frame timing records (frame_ms, draw_calls) and need a combined data quality audit, string normalisation, and numeric ordering in a single low-cost API call. It is ideal for pre-analysis cleanup of game performance logs, up to 200 rows at a time. It does not perform predictions, live lookups, or external data enrichment, so prefer it strictly for offline/local analytical cleaning tasks on supplied data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:32:13.795Z","isFirstParty":false,"canonicalSlug":"frame-timing-analyzer-record-quality-audit-string-normalisation-b5e0d8e5"}