Knowledge Connection Map
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Please install @user_70c2f807/knowledge-connection-map according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem
Study material often arrives as fragments: chapters, videos, or papers can leave concepts memorable in isolation, but hard to connect into a structure. The useful artifact is not another list, but a knowledge connection map that exposes hierarchy, causality, parallels, and contrasts, so a learner can check whether they truly understand how the ideas relate.
How It Works and Limits
The skill works from a topic or a set of concepts. It first confirms the scope and goal, then identifies core and peripheral concepts, and outputs a text-based graph. The typical workflow includes:
- Defining the subject, concept list, and objective, such as understanding relations, exam review, or gap checking
- Classifying relation types: hierarchy, causality, parallel, and contrast
- Rendering an indented text structure with relation notes
- Extending related nodes and suggesting a learning path from main branches to details
It fits subjects like machine learning, mechanics, or probability when the goal is structuring material and supporting active recall. It does not generate visual diagrams; deep domain relations should be confirmed by the user, and it should be treated as study support rather than a replacement for textbooks or courses.
Use Cases
- Before a machine learning exam, map supervised, unsupervised, and reinforcement concepts into a text graph with hierarchy and contrast notes.
- Organize high-school mechanics formulas and concepts into a reviewable main-trunk knowledge map with a learning order.
- Given a set of unfamiliar subject terms, identify core concepts and expand related concepts into a connected network.
- During review, hide the text map, redraw the first-level core concepts, and check whether each relation can be explained.
Best For
- Students preparing for machine learning exams who need to organize scattered algorithm concepts into hierarchy, contrast, and recallable relations.
- Engineers self-studying cross-domain concepts who need to expand a concept set into an actively recallable network.
- Teachers guiding review who need a text-based main-trunk map and a beginner-to-advanced learning order.
- Graduate-exam candidates who need to identify causal derivation paths between core formulas and concepts.
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