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Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
📊 Stats
Stars43,758
Forks6,464
LanguagePython
LicenseMIT
📦 Ships with agentic-awesome-skills
</> SKILL.md
arrowspace.SKILL.md
---name: arrowspace
description: "Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings."
category: data
risk: safe
source: community
source_repo: Genefold/arrowspace-skills
source_type: community
date_added: "2026-06-25"
author: Genefold AI
license: Apache-2.0
license_source: "https://github.com/Genefold/arrowspace-skills/blob/main/LICENSE"
tags: [vector-search, spectral-analysis, graph-laplacian, embeddings, lambda-tau]
tools: [claude, cursor, codex, gemini, opencode]
---# ArrowSpace
Spectral vector search that augments nearest-neighbour search with graph Laplacian features. Computes a Laplacian over the item graph and uses the Rayleigh quotient to produce a λτ (lambda-tau) score per item, enabling search that respects both semantic similarity and structural role.
## When to Use This Skill
- Cosine or L2 similarity misses latent structure in your embeddings
- You want graph-based retrieval with spectral awareness
- You need to characterise the spectral properties of an embedding space
