Map problem structures across domains using category-theoretic morphisms.
npx skills add https://github.com/pinren/morphism-mapper --skill morphism-mapper
Please help me install this Agent Skill. Skill: morphism-mapper Source: https://github.com/pinren/morphism-mapper/tree/main Command: npx skills add https://github.com/pinren/morphism-mapper --skill morphism-mapper
Morphism Mapper maps a problem's structure (Domain A) to distant Domains B using category-theoretic morphisms to generate cross-domain innovations.
It enables AI agents to reason across disciplines by structuring problems as Objects and Morphisms and pulling back practical proposals.
Example: Use morphism-extract "如何扩展到新市场" to extract a category skeleton, then morphism-map <domain> to begin mapping, and morphism-synthesize to generate proposals.
High-intent search queries and answers about installing and using this skill.
Cross-domain mapping structures a problem as objects and morphisms, then translates that skeleton to a distant domain to generate innovative proposals. You need it when seeking breakthrough insights by applying patterns from unfamiliar disciplines to your current challenge.
Morphism mapping works through a four-stage workflow: extract your problem's category skeleton, select a target domain, perform the functorial mapping, and pull back to synthesize practical proposals. You can also apply advanced modules like Yoneda Probe or Adjoint Balancer for deeper analysis.
Cross-domain mapping supports 31 built-in domains and fully custom domains. You can define your own target domain through the knowledge-base management system, allowing flexible mapping to any discipline relevant to your specific innovation context.
Cross-domain mapping includes built-in safety checks for ethics and compliance during the synthesis phase. These checks evaluate generated proposals against ethical constraints and compliance requirements before returning the final cross-domain innovation outputs.
The Yoneda Probe is an advanced module that examines a domain's structure from external perspectives to reveal hidden properties. It enhances domain mapping by exposing implicit relational structures within the target domain before the functorial mapping occurs.
Cross-domain mapping via category theory requires extracting a valid problem skeleton, meaning poorly structured or highly ambiguous problems may resist functorial translation. Additionally, synthesized proposals require domain expertise to validate practical feasibility before implementation.
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