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Introduces Skill-α, a reinforcement learning method for progressively generating high-quality agent skills by treating skill generation as sequential editing with a rollback reward. It improves downstream success rates over existing baselines on CL-Bench and tau2-bench.
This paper from MIT and Harvard explores automating SKILL.md generation by mining GUI interaction trajectories, finding that clusters are readable but do not improve policy performance across domains.
mimeo is an open-source Python tool that automatically generates AGENTS.md or SKILL.md files from an expert's body of work, helping coding agents install better defaults.
MIND-Skill is a new framework introduced in this research paper that automates the generation of high-quality, reusable agent skills using multi-agent induction and deduction with quality guarantees via TextGrad optimization.
SkillLearnBench introduces the first benchmark for evaluating continual skill learning in LLM agents across 20 real-world tasks, revealing that no method dominates and scaling LLMs does not guarantee better skills.