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SKILL.md

Systematic extraction and analysis of entities, relationships, and ontological structures from unstructured text—enhanced with categorical metagraph compression enabling scale-invariant representation through structural equivalence, k-bisimulation summarization, and quotient constructions that preserve query-answering capabilities while achieving dramatic size reductions.

Structural equivalence enables compression through a precise mechanistic chain:

Graphs with large automorphism groups have lower complexity because only one representative from each orbit needs encoding. For highly symmetric structures, compression can reach n/log n factor.

在提取實體和關係、構建本體、壓縮大圖或分析知識結構時使用 - 提供基於結構等價的壓縮，實現 57-95% 的尺寸減小、k-bisimulation 總結、分類商構造和具有尺度不變屬性的元圖分層建模。支持通過圖形拓撲指標進行遞歸細化，包括 |R|/|E|比率和自同構分析。 來源：zpankz/mcp-skillset。

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