Vector Hyperbolic
Embeds hierarchical data in the Poincaré ball model using ruvector@0.2.25, though you'll need to handle the hyperbolic projection yourself since that version lacks a native Poincaré flag. You generate standard ONNX embeddings first, then normalize them into the unit ball and calculate geodesic distances in your own code. The payoff is logarithmic distance scaling that captures tree depth efficiently, useful for dependency graphs, class hierarchies, or any nested structure where "closer to the root" versus "deep in a subtree" matters semantically. Worth the extra projection step if you're mapping codebases or taxonomies where Euclidean embeddings waste dimensions on hierarchy.
- skills.sh installs
- 725
- GitHub stars
- 81.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add ruvnet/ruflo --skill vector-hyperbolic --agent claude-codeInstalls into .claude/skills of the current project.
Files
Vector Hyperbolic
Embed hierarchical data in the Poincare ball model using ruvector.
When to use
Use this skill when your data has inherent hierarchy — dependency trees, module structures, taxonomies, org charts, ontologies. Hyperbolic space captures hierarchical distances with far fewer dimensions than Euclidean embeddings.
Steps
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- Repository
- github.com/ruvnet/ruflo
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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