エージェント設計エージェント：Memento-Skillsによる自律的なタスク特化型エージェントの構築

We introduce emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an emph{agent-designing agent}: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with emph{stateful prompts}, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions. Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the emph{Read--Write Reflective Learning} mechanism introduced in emph{Memento~2}~cite{wang2025memento2}. In the emph{read} phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the emph{write} phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables emph{continual learning without updating LLM parameters}, as all adaptation is realised through the evolution of externalised skills and prompts. Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to emph{design agents end-to-end} for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the emph{General AI Assistants} benchmark and emph{Humanity's Last Exam} demonstrate sustained gains, achieving 26.2% and 116.2% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento-Teams/Memento-Skills.