Agent skill · data analytics · sickn33
arrowspace
Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
Why this skill is useful
Provides domain-specific knowledge on spectral vector search techniques that the AI wouldn't reliably generate on its own.
What it needs
Requires arrowspace installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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 You are building RAG pipelines where contextual role matters alongside semantic content How It Works Step 1: Install and import Step 2: Prepare your data Pass an (N, d) float64 NumPy array of embedding vectors: Step 3: Configure graph parameters Step 4: Query These are per-item spectral signatures, not graph eigenvalues or query-dependent relevance scores. Sorting them alone is not nearest-neighbor retrieval; do not infer closeness to a query or graph centrality from a large signature. Examples Example 1: Basic spectral retrieval Example 2: Compare spectral vs cosine ranking Best Practices ✅ Normalise embeddings to unit norm before passing to ArrowSpace ✅ Treat graph parameters as corpus-dependent; inspect disconnected/degenerate scores and tune using an explicitly selected objective ✅ Choose k relative to the actual corpus and library constraints; N/50 is not a universal quality guarantee ✅ In the reviewed 0.28 API, sigma=None uses the eps-dependent default; it is not automatic distribution fitting ✅ Small corpora can illustrate the API, but retrieval quality requires corpus-appropriate evaluation; ten items is not a mathematical validity threshold ❌ Don't use for real-time streaming data (ArrowSpace is batch-oriented) Limitations API examples target the reviewed 0.28 interface; immutable upstream skill documents the builder, graph return value and query-aware search. These examples were checked against source documentation, not executed here. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/arrowspace--b26832