Knowledge Mind Map Generator

Problem

When subject knowledge, chapter systems, formulas, theorems, or book notes exist only as hierarchy names, manual diagrams often become inconsistent, crowded, or hard to compare. This skill turns structured knowledge into a downloadable high-resolution mind map, focusing on how to render hierarchy, distinguish main branches, and attach concise node descriptions.

How It Works

Input can be provided in two forms:
- Indented text: use 2-space indentation for hierarchy, and Concept :: description for definitions, formulas, or key points.
- JSON: describe nodes with name, desc, and children.

Core capabilities include:
- Three layouts: htree for deep left-to-right trees, vtree for wide top-to-bottom overviews, and radial for compact center-out views.
- Two visual styles: classic uses depth-cycled colors and right-angle connectors; buzan uses organic curves, same-hue branch gradients, and reduced font/line weight with depth.
- Multiple palettes: such as vivid, jewel, sunset, ocean, and classic, assigning colors by main branch to reduce cross-branch confusion.

If nodes contain only names, descriptions can be gathered conversationally from SA-LIB, general knowledge sources, or web results, condensed into 1–3 lines of desc, and then rendered. The engine handles structured rendering with descriptions and does not embed a search API.

Boundaries

This skill is best for hierarchical content such as subject knowledge, chapter outlines, exam-point networks, and literature structure. Output is .png, with emphasis on 300 DPI, Noto Sans SC Chinese font, zero sibling/parent overlap, and adaptive canvas sizing. If the underlying knowledge is highly graph-like rather than tree-like, the structure may still need manual cleanup before mapping.