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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.

在提取实体和关系、构建本体、压缩大图或分析知识结构时使用 - 提供基于结构等价的压缩，实现 57-95% 的尺寸减小、k-bisimulation 总结、分类商构造和具有尺度不变属性的元图分层建模。支持通过图形拓扑指标进行递归细化，包括 |R|/|E|比率和自同构分析。 来源：zpankz/mcp-skillset。

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