AI Teacher Knowledge Growth Tree

Problem

When reading technical articles, papers, or product docs, the common pain point is not that content is too short, but that concept density is high: one term leads to several new terms, and continuing to ask can drift away from the main thread, while pausing to record adds friction. General Q&A tools usually return a one-off explanation, without preserving context or consistently archiving unknown concepts. Users who want a lightweight general-knowledge framework often lack a low-friction, traceable entry point.

How It Works

@user_5b1bdbdf/ai-teacher-skill breaks the workflow into explain, archive, display, and recommend. After a user enters a short query such as What is the entropy increase law? or Explain blockchain simply, the skill first gives a plain-language, boundary-aware explanation without diving into formulas or deep architecture. It then marks the new concept as a knowledge node and records it in a queryable knowledge tree. Commands such as show my knowledge tree present nodes that have already been captured, reducing the pressure caused by a pile of unknown topics. recommend new knowledge suggests adjacent topics based on existing nodes, for example expanding from blockchain to consensus mechanisms or on-chain storage.

The skill emphasizes cross-platform compatibility and low system dependency, supporting macOS, Linux, and Windows, with a runtime requirement of Python 3.6+. Its value is not to replace a professional learning path, but to turn scattered concepts into a browsable, structured set of entries.

Boundaries

It is suitable for general-knowledge pop science, term explanation, introductory learning, and building a knowledge map. It is not designed for rigorous proofs, reproducible experiments, or domain-specific decision making. If the user needs implementation details, performance limits, compliance risk, or source-code-level mechanisms, a specialized source or domain tool should be used. The knowledge tree reflects only recorded nodes and does not represent a complete knowledge system; automatic recommendations may also favor surface-level associations, so users should curate the next reading direction.