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Agent skill

Embeddings

by ruvnet in ruvnet/ruflo

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support.

MITAuto-check passedAI & LLM Engineering

Install Embeddings

skills CLI
$ npx skills add ruvnet/ruflo --skill embeddings -a claude-code

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

GitHub CLI
$ gh skill install ruvnet/ruflo embeddings --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/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/embeddings .claude/skills/embeddings && 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
embeddings
GitHub stars
74k
Used in
3 other repos
Token cost
~455 tokens
SKILL.md length
101 words
Files
1
Skills in repo
264
Repo updated
First seen
Licence
MIT

At a glance

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support.

  • Works in 4 steps: Use HNSW for large pattern databases → Enable quantization for memory efficiency → Use hyperbolic for hierarchical… → …
  • : semantic search
  • SKILL.md covers Purpose, Features, Commands and Memory Integration, plus 2 more sections
  • Calls npx

What it does

Embeddings is an agent skill from ruvnet/ruflo. Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

Its SKILL.md is about 460 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. It works with SQL. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

When your agent uses it

  • : semantic search
  • Pattern matching
  • Similarity queries
  • Knowledge retrieval

Example prompts

  • “/embeddings”

Requirements

  • Node.js

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Use HNSW for large pattern databases
  2. Enable quantization for memory efficiency
  3. Use hyperbolic for hierarchical relationships
  4. Normalize embeddings for consistency

What it can do on your machine

Read from SKILL.md and the folder at commit 6051f67. 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:

    • npx

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

  • Network

    No URLs in SKILL.md. Its commands use npx, 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

Embeddings loads about 455 tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 101 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~455

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 ruvnet/ruflo at commit 6051f67, republished under its MIT licence (© ruvnet). 101 words, ~455 tokens.

Download SKILL.mdSave it as .claude/skills/embeddings/SKILL.md (or your agent's skills folder).
name
embeddings
description
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

Embeddings Skill

Purpose

Vector embeddings for semantic search and pattern matching with HNSW indexing.

Features

FeatureDescription
sql.jsCross-platform SQLite persistent cache (WASM)
HNSW150x-12,500x faster search
HyperbolicPoincare ball model for hierarchical data
NormalizationL2, L1, min-max, z-score
ChunkingConfigurable overlap and size
75x fasterWith agentic-flow ONNX integration

Commands

Initialize Embeddings
bash
npx claude-flow embeddings init --backend sqlite
Embed Text
bash
npx claude-flow embeddings embed --text "authentication patterns"
Batch Embed
bash
npx claude-flow embeddings batch --file documents.json
bash
npx claude-flow embeddings search --query "security best practices" --top-k 5

Memory Integration

bash
# Store with embeddings
npx claude-flow memory store --key "pattern-1" --value "description" --embed

# Search with embeddings
npx claude-flow memory search --query "related patterns" --semantic

Quantization

TypeMemory ReductionSpeed
Int83.92xFast
Int47.84xFaster
Binary32xFastest

Best Practices

  1. Use HNSW for large pattern databases
  2. Enable quantization for memory efficiency
  3. Use hyperbolic for hierarchical relationships
  4. Normalize embeddings for consistency

© ruvnet, MIT. 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 .agents/skills/embeddings of ruvnet/ruflo.

Open the folder on GitHubat commit 6051f67

Used in 3 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Embeddings 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.

Embeddings compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Embeddings this skillruvnet/ruflo74k3 repos~455Automated safety check: PassMIT
DBoracle/skills873—~1.4kAutomated safety check: PassUPL-1.0
Google Cloud Solution RAG Enterprise Search Gke Sqldbgoogle/skills21k—~4kAutomated safety check: PassApache-2.0
Supabasesundial-org/awesome-openclaw-skills663—~1.6kAutomated safety check: PassNone
Cortexdbliliang-cn/cortexdb274—~18kAutomated safety check: WarnMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT

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All 264 skills in this repo

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    Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.

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  • Swarm Orchestration

    ruvnet/ruflo

    Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.

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Works with

Categories

Questions about Embeddings

What does Embeddings do?

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. Embeddings is an agent skill from ruvnet/ruflo.js persistence, and hyperbolic support.

When should I use Embeddings?

Embeddings fits situations like: : semantic search; pattern matching; similarity queries; knowledge retrieval.

How do I install Embeddings in Claude Code?

Run `npx skills add ruvnet/ruflo --skill embeddings -a claude-code`. Or copy the skill folder (.agents/skills/embeddings in ruvnet/ruflo) into .claude/skills/embeddings in your project. Claude Code loads it when a task matches its description.

How do I install Embeddings in Codex?

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

Can I use Embeddings 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 ruvnet/ruflo --skill embeddings -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/embeddings, .gemini/skills/embeddings, .github/skills/embeddings and .opencode/skills/embeddings in your project.

What does Embeddings need to run?

Going by SKILL.md and its folder, Embeddings needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Embeddings access the network?

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

Is Embeddings 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 Embeddings use?

Embeddings is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Embeddings use?

About 455 tokens (SKILL.md is roughly 1.8k 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 Embeddings?

Skills that share tags, products or a category with Embeddings: DB (oracle/skills, 873 stars), Google Cloud Solution RAG Enterprise Search Gke Sqldb (google/skills, 21k stars), Supabase (sundial-org/awesome-openclaw-skills, 663 stars) and Cortexdb (liliang-cn/cortexdb, 274 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Embeddings?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,089 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 8, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.