From ruflo-ruvector
Embed hierarchical data via npx [email protected] embed text and project into the Poincare ball in user code (no --model poincare flag in 0.2.25)
How this skill is triggered — by the user, by Claude, or both
Slash command
/ruflo-ruvector:vector-hyperbolic <text> [--model poincare]<text> [--model poincare]This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Embed hierarchical data in the Poincare ball model using `ruvector`.
Embed hierarchical data in the Poincare ball model using ruvector.
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.
npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install [email protected]
--model poincare flag on embed text):
npx -y [email protected] embed text "hierarchical concept" -o concept.vec.json
npx -y [email protected] embed neural --help
For an ad-hoc projection, normalize the 384-dim vector to live inside the unit ball (x_i / (||x|| * (1 + epsilon))) and persist the projected coordinates alongside the original embedding.d(u, v) = arcosh(1 + 2 * ||u-v||^2 / ((1-||u||^2)(1-||v||^2)))
Distance grows logarithmically with tree depth, preserving hierarchy.mcp__claude-flow__memory_store({ key: "hyperbolic-CONCEPT", value: "COORDINATES_AND_NEIGHBORS", namespace: "hyperbolic-embeddings" })| 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 |
npx claudepluginhub erfwn81/velora --plugin ruflo-ruvectorGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.