Skill Introduction

Overview

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)

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community, claude, mcp work faster.

Target users

Developers, testers, and maintainers who handle Development tasks in Focus Code.
Teams that already trust workflows or content from ruvnet.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

Tasks related to github, community, claude, mcp that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

Structured around Development, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, claude, mcp, so it can be filtered by concrete task intent.

Install and use

Install

Copy Install Command

focus install vector-hyperbolic-d1ffb5

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SKILL.md

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name: vector-hyperbolic description: 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) argument-hint: "<text> [--model poincare]" allowed-tools: Bash Read mcpclaude-flowmemory_store mcpclaude-flowmemory_search

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 poincare flag on embed 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):
```
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.
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__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

vector hyperbolic