1 link tagged with all of: microsoft research + benchmarks + agent skills + prompt optimization + skillopt
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SkillOpt is a Microsoft framework that optimizes AI agent skills (prompt documents) using supervised training techniques—epochs, learning rates, validation gates—without modifying model weights. It treats skill documents as trainable state and uses an optimizer model to iteratively edit them based on scored rollouts.
- SkillOpt achieves +23.5 to +24.8 point accuracy gains across six benchmarks and seven models (GPT, Claude, Codex, Qwen), with optimized skills transferring across model scales and execution environments without retraining.
- The system generates compact, deployable skill artifacts (300–2,000 tokens) that add zero inference-time overhead—skills are static documents that run against unmodified target models.
- SkillOpt-Sleep, a nightly offline self-evolution engine, lets local coding agents (Claude Code, Copilot) automatically harvest past sessions, replay tasks, and consolidate validated skills behind held-out validation gates.