EmbeddingsCAUTION
🌊 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
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
3e0c089e8335OBSERVED · 2026-09-28What 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
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
3e0c089e8335full audit observations/trust-audit/skill/ruvnet__embeddings.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-28 | 3e0c089e8335 | CAUTION | B | 89 | first audit |
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.