playbook/antigravity-awesome-skills/plugins/antigravity-awesome-skills-claude/skills/arrowspace/SKILL.md

name, description, category, risk, source, source_repo, source_type, date_added, author, license, license_source, tags, tools

name	description	category	risk	source	source_repo	source_type	date_added	author	license	license_source	tags	tools
arrowspace	Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.	data	safe	community	Genefold/arrowspace-skills	community	2026-06-25	Genefold AI	Apache-2.0	https://github.com/Genefold/arrowspace-skills/blob/main/LICENSE	vector-search spectral-analysis graph-laplacian embeddings lambda-tau	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
• You are building RAG pipelines where contextual role matters alongside semantic content

How It Works

Step 1: Install and import

pip install arrowspace

from arrowspace import ArrowSpaceBuilder
import numpy as np

Step 2: Prepare your data

Pass an (N, d) float64 NumPy array of embedding vectors:

items = np.array([[0.1, 0.2, 0.3],
                  [0.0, 0.5, 0.1],
                  [0.9, 0.1, 0.0]], dtype=np.float64)

Step 3: Configure graph parameters

graph_params = {"eps": 0.2, "k": 6, "topk": 3, "p": 2.0, "sigma": 1.0}
builder = ArrowSpaceBuilder(items, graph_params=graph_params)
aspace = builder.build()

Step 4: Query

lambdas = aspace.lambdas()           # array indexed by insertion order
sorted_res = aspace.lambdas_sorted()  # (score, index) pairs ascending

Higher λτ values indicate items that are both semantically close and structurally central.

Examples

Example 1: Basic spectral retrieval

items = np.random.randn(100, 64).astype(np.float64)
builder = ArrowSpaceBuilder(items, graph_params={"eps": 0.5, "k": 10, "topk": 5, "p": 2.0, "sigma": None})
aspace = builder.build()
scores = aspace.lambdas()
top_indices = np.argsort(scores)[-5:]

Example 2: Compare spectral vs cosine ranking

from sklearn.metrics.pairwise import cosine_similarity
cos_sim = cosine_similarity(items)
cosine_order = np.argsort(cos_sim[0])[::-1]
spectral_order = np.argsort(aspace.lambdas())[::-1]

Best Practices

• ✅ Normalise embeddings to unit norm before passing to ArrowSpace
• ✅ Start with eps proportional to 1/sqrt(dim) and tune from there
• ✅ Use k between 3 and 25 depending on dataset size (rule: N/50)
• ✅ Set sigma=None to auto-select kernel width from distance distribution
• ❌ Don't use with fewer than 10 items (graph structure is not meaningful)
• ❌ Don't use for real-time streaming data (ArrowSpace is batch-oriented)

Limitations

• This skill does not replace environment-specific validation, testing, or expert review.
• ArrowSpace is batch-oriented and not designed for real-time indexing of streaming data.

Common Pitfalls

• Problem: eps is too small, producing a disconnected graph Solution: Increase eps, or set it proportional to 1/sqrt(embedding_dim)

• Problem: k is too large, producing a dense graph with washed-out spectral features Solution: Keep k ≤ 25 for most datasets

Related Skills

• vector-database-engineer — General vector database expertise
• embedding-strategies — Embedding model selection and chunking
• similarity-search-patterns — Semantic search implementation patterns
• hybrid-search-implementation — Combined semantic + keyword search