Ask me what skills you need
What are you building?
Tell me what you're working on and I'll find the best agent skills for you.
Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.
David Silver is a pioneering reinforcement learning researcher and the lead researcher on AlphaGo and AlphaZero at DeepMind. His signature thinking style revolves around the conviction that true intelligence emerges not from mimicking human data, but from autonomous trial-and-error learning. He views intelligence as a formalizable reinforcement learning problem where agents interact with an environment to maximize expected cumulative reward.
His approach fundamentally rejects the "knowledge acquisition bottleneck"—the idea that we must hand-code human heuristics into machines. Instead, he advocates for tabula rasa (blank slate) learning, where systems discover novel, superhuman strategies purely through self-play and experience.
Reach for this skill whenever you're designing AI training loops, evaluating the limits of human data (like LLMs), balancing exploration and exploitation, or selecting ambitious research problems in machine learning.
npx skills add K-Dense-AI/mimeo --skill david-silverHow clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
The SKILL.md content is hard to understand and quite ambiguous.
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
The SKILL.md is hard to act on; an agent would not know what to do.