We run a three-task pipeline sequentially: a CSV analyzer, a text report generator, and a data quality checker, tracking how skills accumulate and reuse increases with each successive task, mirroring the GDPVal benchmark’s Phase 1 design. We use the OpenAI API to perform an AI-powered analysis of our evolved skill library, identifying coverage gaps, potential evolution paths, and recommended new skills to create. We close with a direct cold-versus-warm token comparison that measures real savings by sending the same task with and without skill context, demonstrating concretely how pre-evolved patterns reduce both token cost and response length.
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历史数据表明，当恒生指数重新突破年线后，短期内往往延续回升态势。自2000年以来的统计显示，恒指突破年线后的3个、5个及10个交易日，上涨概率超过90%，期间涨幅中值分别达到2.7%、3.0%和3.8%。
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