What problem does it solve? Teams building algorithm-problem-solving products (Baekjoon/LeetCode/judge-style) lack a shared conceptual skeleton, so learning paths, recommendations, hints, editorials, and progress tracking each invent their own inconsistent view of concepts, tags, and difficulty. ## Core Features & Use Cases - Concept Taxonomy Design: Structures layered taxonomies (foundation, data structure, algorithm family, pattern, technique, problem model) with a canonical leaf-tag vocabulary drawn from CP-Algorithms, USACO Guide, and standard curricula. - Prerequisite & Difficulty Modeling: Provides a starter prerequisite DAG, constraint-to-complexity heuristics, and multi-dimensional difficulty axes (insight, implementation burden, proof burden, language burden) instead of a single difficulty number. - Tag Reveal & Normalization Policy: Defines spoiler-aware tag reveal levels and normalization mappings across solved.ac, Codeforces, LeetCode, and AtCoder difficulty scales. - Use Case: A planner asks how to model DP, graph, and greedy concepts so the recommendation engine can suggest gap-filling problems; the Skill produces a concept map with prerequisites, common mistakes, reveal policies, and MVP scope boundaries. ## Quick Start Ask the Skill to design an algorithm concept taxonomy and prerequisite map for your problem-solving product's recommendation and hint features.
