arrowspace
Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
Profile →npx skills add sickn33/agentic-awesome-skills --skill arrowspace --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash pip install arrowspace ``` ```python from arrowspace import ArrowSpaceBuilder import numpy as np ``` ### Step 2: Prepare your data Pass an (N, d) float64 NumPy array of embedding vectors: ```python 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 ```python 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 ```python la
- When to Use This Skill
- How It Works
- Step 1: Install and import
- Step 2: Prepare your data
- Step 3: Configure graph parameters
- Step 4: Query
- Examples
- Example 1: Basic spectral retrieval
- Example 2: Compare spectral vs cosine ranking
- Best Practices
- Limitations
- Common Pitfalls
- Related Skills
pip install arrowspace
What does the arrowspace skill do?
Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill arrowspace --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.