Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ruvnet/ruflo/agentdb-vector-searchnpx skills add ruvnet/ruflo --skill agentdb-vector-searchgit clone --depth 1 https://github.com/ruvnet/rufloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ruvnet/ruflo/agentdb-vector-search)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agentdb-vector-search"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agentdb-vector-search.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.02392 |
| Opus 5 | $0.00020 | $0.01196 |
| Sonnet 5 | $0.00008 | $0.00478 |
| Haiku 4.5 | $0.00004 | $0.00239 |
Grade A, and why
AgentDB Vector Search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to AgentDB Vector Search — 60 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentDB Vector Search
What This Skill Does
Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow or standalone)
- OpenAI API key (for embeddings) or custom embedding model
Quick Start with CLI
Initialize Vector Database
# Initialize with default dimensions (1536 for OpenAI ada-002)
npx agentdb@latest init .$vectors.db
# Custom dimensions for different embedding models
npx agentdb@latest init .$vectors.db --dimension 768 # sentence-transformers
npx agentdb@latest init .$vectors.db --dimension 384 # all-MiniLM-L6-v2
# Use preset configurations
npx agentdb@latest init .$vectors.db --preset small # <10K vectors
npx agentdb@latest init .$vectors.db --preset medium # 10K-100K vectors
npx agentdb@latest init .$vectors.db --preset large # >100K vectors
# In-memory database for testing
npx agentdb@latest init .$vectors.db --in-memory
Query Vector Database
# Basic similarity search
npx agentdb@latest query .$vectors.db "[0.1,0.2,0.3,...]"
# Top-k results
npx agentdb@latest query .$vectors.db "[0.1,0.2,0.3]" -k 10
# With similarity threshold (cosine similarity)
npx agentdb@latest query .$vectors.db "0.1 0.2 0.3" -t 0.75 -m cosine
# Different distance metrics
npx agentdb@latest query .$vectors.db "[...]" -m euclidean # L2 distance
npx agentdb@latest query .$vectors.db "[...]" -m dot # Dot product
# JSON output for automation
npx agentdb@latest query .$vectors.db "[...]" -f json -k 5
# Verbose output with distances
npx agentdb@latest query .$vectors.db "[...]" -v
Import/Export Vectors
# Export vectors to JSON
npx agentdb@latest export .$vectors.db .$backup.json
# Import vectors from JSON
npx agentdb@latest import .$backup.json
# Get database statistics
npx agentdb@latest stats .$vectors.db
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 340 lines · 41 tokens per session scan A 4a9292a66d5a
AgentDB Vector Search is a skill published in the GitHub repository ruvnet/ruflo (70,734 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 2,392 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to AgentDB Vector Search, differing in 60 lines, and is treated as a copy.
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