vector-hyperbolic
Embed hierarchical data via npx ruvector@0.2.25 embed text and project into the Poincare ball in user code (no --model poincare flag in 0.2.25)
How do I install this agent skill?
npx skills add https://github.com/ruvnet/ruflo --skill vector-hyperbolicIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill is safe. It standardly installs and utilizes the ruvector package at a pinned version (0.2.25) from the public npm registry to perform hyperbolic vector embeddings and Poincare ball projections.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
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
- Ensure ruvector@0.2.25 is available:
npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.25 - Generate a base ONNX embedding (ruvector@0.2.25 does not expose a
--model poincareflag onembed text):npx -y ruvector@0.2.25 embed text "hierarchical concept" -o concept.vec.json - Project into the Poincare ball in your own code (or via the experimental neural substrate):
For an ad-hoc projection, normalize the 384-dim vector to live inside the unit ball (npx -y ruvector@0.2.25 embed neural --helpx_i / (||x|| * (1 + epsilon))) and persist the projected coordinates alongside the original embedding. - Geodesic distance:
d(u, v) = arcosh(1 + 2 * ||u-v||^2 / ((1-||u||^2)(1-||v||^2)))Distance grows logarithmically with tree depth, preserving hierarchy. - Store results:
mcp__plugin_ruflo-core_ruflo__memory_store({ key: "hyperbolic-CONCEPT", value: "COORDINATES_AND_NEIGHBORS", namespace: "hyperbolic-embeddings" })
Caveats
- ruvector@0.2.25 has no first-class Poincare ball CLI flag. Treat hyperbolic projection as a post-processing step over a standard ONNX embedding.
- If you need a hyperbolic search index, store projected coordinates in AgentDB and compute geodesic distance in your own retrieval code.
Poincare ball properties
| Property | Meaning |
|---|---|
| Norm close to 0 | Generic, root-level concept |
| Norm close to 1 | Specific, leaf-level concept |
| Small geodesic distance | Closely related in hierarchy |
| Large geodesic distance | Distant or different subtrees |
Use cases
- Dependency analysis: embed module imports to find tightly coupled subtrees
- Code architecture: map class hierarchies to discover structural patterns
- Knowledge organization: embed concepts to reveal taxonomic relationships
- Codebase navigation: find most specific/general modules relative to a query
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/ruvnet/ruflo/vector-hyperbolic">View vector-hyperbolic on skillZs</a>