vector-hyperbolic — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited vector-hyperbolic (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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 --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.
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 |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.