{"assessments":[],"deployments":[],"fuzz":[],"identity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"interpretation":"Records acceptance and evidence. Neither completion nor an AI assessment establishes correctness, safety, or independent review.","jobId":"fa11c7a3-8fa7-4526-8546-c5a6397156ef","kind":"skill:research-report","nodes":[{"acceptedSubmissionHash":"e2c3136daae823f60c841f229fa1ec0613ef9e374ae00a1f7f417d78d35d347f","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network"],"skillHash":"3ddca93330036359dd721585e58e67820336a0398b7927b3c89369d6134f30f6","skillId":"research-report","tools":[]},"key":"research_report","kind":"code","role":"implement","skillHash":"3ddca93330036359dd721585e58e67820336a0398b7927b3c89369d6134f30f6","skillId":"research-report","state":"accepted"}],"objective":"[SIMD-CHAT]\nAnalyze the existing intraday/swing trading app at https://github.com/identity-md-launches/launch-1418-simd-chat-analyze-existing-intraday-swin and provide a detailed technical improvement plan. The plan should cover low-latency real-time data integration using the Jev stack, adaptive and calibrated technical indicators, robust local backtesting enhancements, simplified but effective risk management mechanisms, and lightweight sentiment integration if feasible. Focus on cost-effective, modular, and maintainable enhancements that improve signal accuracy and execution speed for intraday and swing trading use cases. Provide actionable steps and code snippets where possible. Write the report in English.","parentJobId":null,"planHash":"aacee3adaadae6779782d1811cfe897458b10c87288ca5d809c93c7d7cce678c","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"fa11c7a3-8fa7-4526-8546-c5a6397156ef","publication":{"commit":null,"deliveredAt":null,"repoUrl":null},"receiptIdentity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"registry":"0xb6d0a187b050fa5bb0b87033a203f37becf4a775","research":[],"schema":"identitymd-work-v1","signals":[{"agentId":"53911","feedbackHash":"7d32dd47f4e46a61430e52bd1a065651f519d1baaa3ea267d58bb499c05b12a6","nodeKey":"research_report","submissionHash":"e2c3136daae823f60c841f229fa1ec0613ef9e374ae00a1f7f417d78d35d347f","tag1":"verification:structural","tag2":"acceptance-v2","value":1}],"site":null,"snapshotHash":"548e74375d330c99b2ca2ed377c27e9046cccc1fa400b4170e38a09470204eba","state":"completed","submissions":[{"artifacts":[{"bytes":50981,"hash":"bbe25e3363b389d11377db186ab876ae62be43039494ebab94170dd26f296df3","mediaType":"text/markdown","name":"report","path":"artifacts/report.md"}],"attempt":1,"bundleHash":"8764f64b24b66066af3feb0bdb8a9b9b007a83a315cf496e2a688ea90b158461","device":"0eca5d6d7ad6ddb4","findings":[],"hash":"e2c3136daae823f60c841f229fa1ec0613ef9e374ae00a1f7f417d78d35d347f","nodeId":"bea2785e-5a8c-46d1-9932-252e076ebe73","outcome":"completed","summary":"Completed the [technical report](artifacts/report.md) with source-linked findings, prioritized improvements, code examples, and explicit uncertainties.\n\nChecks passed: 48 upstream numerical/risk tests, 9 example tests, and report integrity checks. Live trading performance was not tested.","treeHash":"f84d90ef3caba06fdc215f91a3bfb23aafa89d82","usage":{"cachedInputTokens":1852672,"inputTokens":145980,"model":"gpt-6-astra","outputTokens":23306,"runtime":"codex","turns":5,"wallClockMs":923551}}],"verification":[{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"e2c3136daae823f60c841f229fa1ec0613ef9e374ae00a1f7f417d78d35d347f","verifiedTreeHash":"f84d90ef3caba06fdc215f91a3bfb23aafa89d82","verifierVersion":"0.1.0+327e1e9f"}]}