{"uid":"cap_30EpS2Hakb_45o5CNtF0K","slug":"frame-timing-analyzer-record-quality-audit-string-normalisation-b28e0543","name":"Frame Timing Analyzer — Record Quality Audit, String Normalisation & Category Frequencies","description":"Frame timing — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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-01-195?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}}},"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_d56fitpjXxtVhBkTLkZOd","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 three local analytical reports on frame timing data (frame_ms, draw_calls): a record quality audit, string normalisation, and category frequency counts — up to 200 rows per call.","exampleAgentPrompt":"Can you audit the quality of these 50 frame timing records from our game — check for missing values, normalise the game_area strings, and give me category frequencies for the game areas? Here are the rows with row_id, date, game_area, frame_ms, and draw_calls.","exampleUseCases":[{"title":"Game performance data quality check","prompt":"I have 120 frame timing records from our game build — each has a row_id, date, game_area, frame_ms in milliseconds, and a draw_calls count. Can you run a record quality audit on them, normalise the game_area labels, and show me how often each category appears?"},{"title":"Identifying null frame timings before pipeline","prompt":"Before I load this batch of 80 frame timing rows into our pipeline, can you audit them for missing or invalid frame_ms and draw_calls values, normalise the game_area strings, and tell me the frequency of each game area category?"},{"title":"Summarising draw call distribution by game area","prompt":"Here are 200 frame timing entries from our latest build — can you check the record quality, clean up the game_area names, and give me a frequency breakdown of how many records belong to each game area?"}],"resultDescription":"Returns three analytical reports: (1) a record quality audit flagging missing, null, or malformed values across rows; (2) normalised versions of string fields such as game_area; and (3) category frequency counts showing how often each distinct category value appears — all computed locally against the supplied data, no external lookups.","failureModes":["Exceeds 200-row limit — request rejected","Missing required fields (row_id, game_area, date, frame_ms, draw_calls) in any row","date field not matching YYYY-MM-DD pattern — validation error","row_id empty or exceeds 80 characters — schema rejection","Numeric values outside allowed range (±1e12) — schema rejection","Empty rows array — minimum 1 item not met"],"whenToPreferThis":"Choose this endpoint when you need three bundled data-quality and summarisation operations (quality audit + string normalisation + category frequencies) on game frame timing data (frame_ms and draw_calls) in a single low-cost call. It is best suited for offline or batch quality checks on datasets up to 200 rows where no live lookup, prediction, or external action is needed. Prefer it over general analytics services when you specifically need all three report types together for game performance telemetry.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T00:39:25.393Z","isFirstParty":false,"canonicalSlug":"frame-timing-analyzer-record-quality-audit-string-normalisation-b28e0543"}