What problem does it solve? Researchers studying computational topology, abstract algebra, probability, Lean, or Manim need structured study materials that stay clearly separated from research outputs, so learning artifacts never contaminate the research record or get cited as evidence. ## Core Features & Use Cases - Structured Learning Outputs: Produces concept ladders, exercise sets, worked solutions, Lean formalization drills, Manim animation outlines, reading guides, and prerequisite repair paths. - Research/Learning Separation: Enforces rules that keep study material out of results directories, prevents toy examples from becoming claims about real data, and blocks learning artifacts from being cited as literature. - Use Case: A researcher wants to build up the algebra background behind a stability theorem. The Skill creates a concept ladder with one exercise and one worked example per rung, filed in the learning workspace and marked as study material, without generating any research claims. ## Quick Start Create a concept ladder with exercises and worked examples for persistent homology prerequisites, stored as learning material outside the research tree.
