Vector Hyperbolic
- 635 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
vector-hyperbolic is a ruflo Claude skill that embeds hierarchical data into the Poincare ball model using ruvector@0.2.25 for developers who need hyperbolic geometry operations on dependency trees, taxonomies, and ontol
About
vector-hyperbolic is a ruflo skill that generates high-dimensional embeddings and projects hierarchical data into the Poincare ball using ruvector@0.2.25. The skill targets structures like dependency trees, module hierarchies, org charts, taxonomies, and ontologies where hyperbolic space captures hierarchical distances in fewer dimensions than Euclidean embeddings. Agents run npx ruvector embed commands and store results via Claude-flow memory tools. Developers reach for vector-hyperbolic when similarity search or distance metrics must respect tree-like hierarchy rather than flat vector geometry.
- Converts vectors into hyperbolic space for richer semantic representations
- Provides fast similarity search and nearest-neighbor operations on embeddings
- Exposes reusable MCP-compatible tools for any agentic coding session
- Reduces token usage by enabling geometric reasoning instead of brute-force comparison
- Works with any embedding model output (OpenAI, Voyage, local, etc.)
Vector Hyperbolic by the numbers
- 635 all-time installs (skills.sh)
- +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,541 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 635 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you embed hierarchical data in hyperbolic space?
Generate high-dimensional embeddings and perform hyperbolic geometry operations inside Claude-powered agent workflows.
Who is it for?
Developers embedding dependency trees, taxonomies, or ontologies who need Poincare ball projections via ruvector in agent workflows.
Skip if: Flat unstructured text search or projects requiring standard Euclidean embeddings without hierarchical structure.
When should I use this skill?
The user has hierarchical data like dependency trees or taxonomies and needs hyperbolic embeddings via ruvector Poincare ball projection.
What you get
Poincare ball embeddings stored in Claude-flow memory for hierarchical similarity and distance queries.
- Poincare ball embeddings
- stored vector representations
By the numbers
- Uses ruvector@0.2.25 for embedding and Poincare ball projection
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
1. Ensure ruvector@0.2.25 is available:
npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.252. Generate a base ONNX embedding (ruvector@0.2.25 does not expose a --model poincare flag on embed text):
npx -y ruvector@0.2.25 embed text "hierarchical concept" -o concept.vec.json3. Project into the Poincare ball in your own code (or via the experimental neural substrate):
npx -y ruvector@0.2.25 embed neural --helpFor 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. 4. 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. 5. Store results: mcp__claude-flow__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
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What ruvector version does vector-hyperbolic use?
vector-hyperbolic uses ruvector@0.2.25, embedding text via npx ruvector embed and projecting into the Poincare ball model in user code without a --model poincare CLI flag.
What data suits vector-hyperbolic embeddings?
vector-hyperbolic suits inherently hierarchical data such as dependency trees, module structures, taxonomies, org charts, and ontologies where hyperbolic distance outperforms flat Euclidean vectors.