Atlas / Skills / ruvnet / Embeddings

EmbeddingsCAUTION

skills/ruvnet/embeddings

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Verdict
CAUTION
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
73,410
01

Overview

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Read from source at commit 3e0c089e8335OBSERVED · 2026-09-28
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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

| Feature | Description |
|---------|-------------|
| **sql.js** | Cross-platform SQLite persistent cache (WASM) |
| **HNSW** | 150x-12,500x faster search |
| **Hyperbolic** | Poincare ball model for hierarchical data |
| **Normalization** | L2, L1, min-max, z-score |
| **Chunking** | Configurable overlap and size |
| **75x faster** | With 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
```

### Semantic Search
```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

| Type | Memory Reduction | Speed |
|------|-----------------|-------|
| Int8 | 3.92x | Fast |
| Int4 | 7.84x | Faster |
| Binary | 32x | Fastest |

## 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
03

Trust audit

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (4)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
crates
crates
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/agents
plugin/agents
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/commands
plugin/commands
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/skills
plugin/skills
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-09-28 · audit v0.4.1 · source sha 3e0c089e8335full audit observations/trust-audit/skill/ruvnet__embeddings.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-09-283e0c089e8335CAUTIONB89first audit
05

Questions

What does the Embeddings skill do?

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Is Embeddings safe to install?

With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.

What can Embeddings access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (3e0c089e8335), read on 2026-09-28. The repository is watched, and a new audit runs when it changes — this is the first audit.

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