skillopt-9250d08f·2 events·first seen Aliases: SkillOpt
Microsoft has open-sourced SkillOpt, a Python framework that optimizes natural-language skills for frozen LLM agents without modifying model weights. The system uses trajectory-driven edits and validation-gated updates to produce deployable best_skill.md artifacts. The approach is relevant to agent systems that need reusable, inspectable skill libraries without fine-tuning. The repository has accumulated over 11,600 stars with strong recent momentum (+261 today).
SkillOpt introduces a principled optimization framework for agent skills, treating the skill document as an external trainable state analogous to model weights. A separate optimizer model converts scored rollouts into bounded edits (add/delete/replace) on a skill document, accepting only edits that improve held-out validation scores. Evaluated across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt achieves best or tied performance on all 52 evaluated cells, lifting GPT-5.5 no-skill accuracy by up to +24.8 points inside the Codex agentic loop. Optimized skill artifacts also transfer across model scales and execution environments without further optimization.