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From hyrex-ruvector
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)
npx claudepluginhub akhilyad/deployy --plugin hyrex-ruvectorHow this skill is triggered — by the user, by Claude, or both
Slash command
/hyrex-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`.
Guides technical evaluation of code review feedback: read fully, restate for understanding, verify against codebase, respond with reasoning or pushback before implementing.
Share bugs, ideas, or general feedback.
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 ruvector@0.2.25
--model poincare flag on embed text):
npx -y ruvector@0.2.25 embed text "hierarchical concept" -o concept.vec.json
npx -y ruvector@0.2.25 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__hyrex__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 |