Abstract
We introduce *Memento-Skills*, a generalist, continually-learnable LLM agent system that functions as an *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 *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 *Read--Write Reflective Learning* mechanism introduced in *Memento~2*~\cite\{wang2025memento2\}. In the *read* phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the *write* phase, the agent updates and expands its skill library based on new experience. This closed-loop design