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Agent skill

Arrowspace

by sickn33 in sickn33/agentic-awesome-skills

Spectral vector search using graph Laplacian eigenstructure.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Arrowspace

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill arrowspace -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills arrowspace --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/arrowspace .claude/skills/arrowspace && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
arrowspace
GitHub stars
47k
Used in
1 other repo
Token cost
~963 tokens
SKILL.md length
294 words
Files
1
Skills in repo
1,493
Repo updated
First seen
Licence
Apache-2.0

At a glance

Spectral vector search using graph Laplacian eigenstructure.

  • Works in 4 steps: Install and import → Prepare your data → Configure graph parameters → …
  • Cosine/L2 similarity misses latent structure in your embeddings
  • SKILL.md covers When to Use This Skill, How It Works, Examples and Best Practices, plus 3 more sections
  • Calls pip

What it does

Arrowspace is an agent skill from sickn33/agentic-awesome-skills. Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Embeddings and Vector databases. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.

When your agent uses it

  • Cosine/L2 similarity misses latent structure in your embeddings
  • Tasks that involve Embeddings
  • Tasks that involve Vector databases

Example prompts

  • “/arrowspace”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Install and import
  2. Prepare your data
  3. Configure graph parameters
  4. Query

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Arrowspace loads about 963 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 294 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~963

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its Apache-2.0 licence (© sickn33). 294 words, ~963 tokens.

Download SKILL.mdSave it as .claude/skills/arrowspace/SKILL.md (or your agent's skills folder).
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

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
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
python
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
python
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

  • 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

© sickn33, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/arrowspace of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Arrowspace next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Arrowspace compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Arrowspace this skillsickn33/agentic-awesome-skills47k1 repos~963Automated safety check: PassApache-2.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Pgvector Semantic Searchtimescale/pg-aiguide1.9k—~3.8kAutomated safety check: PassApache-2.0
RAG Implementationwshobson/agents40k9 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about Arrowspace

What does Arrowspace do?

Spectral vector search using graph Laplacian eigenstructure. Arrowspace is an agent skill from sickn33/agentic-awesome-skills. Spectral vector search using graph Laplacian eigenstructure.

When should I use Arrowspace?

Arrowspace fits situations like: cosine/L2 similarity misses latent structure in your embeddings; tasks that involve Embeddings; tasks that involve Vector databases.

How do I install Arrowspace in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill arrowspace -a claude-code`. Or copy the skill folder (skills/arrowspace in sickn33/agentic-awesome-skills) into .claude/skills/arrowspace in your project. Claude Code loads it when a task matches its description.

How do I install Arrowspace in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill arrowspace -a codex`. Or copy the skill folder (skills/arrowspace in sickn33/agentic-awesome-skills) into .agents/skills/arrowspace in your project. Codex loads it when a task matches its description.

Can I use Arrowspace in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill arrowspace -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/arrowspace, .gemini/skills/arrowspace, .github/skills/arrowspace and .opencode/skills/arrowspace in your project.

What does Arrowspace need to run?

Going by SKILL.md and its folder, Arrowspace needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Arrowspace access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Arrowspace safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Arrowspace use?

Arrowspace is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Arrowspace use?

About 963 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Arrowspace?

Skills that share tags, products or a category with Arrowspace: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars) and Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Arrowspace?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.