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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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