We implement an instrumented workflow for Microsoft SkillOpt end to end. We set up the repository, connect OpenAI-compatible model access, and configure the optimizer and target models. We evaluate the original seed skill as a baseline, then run a real optimization loop with rollout, reflection, aggregation, selection, updating, and validation-based gating. We inspect training history, visualize accuracy, edit-budget behavior, and token usage, then compare the evolved skill against the baseline.
The post A Coding Implementation on Microsoft SkillOpt for Instrumented Prompt Optimization, Skill Evolution Analysis, and Baseline Comparison appeared first on MarkTechPost.
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