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 skills add shyftlabs/continuum --skill agentdb-vector-searchgit clone --depth 1 https://github.com/shyftlabs/continuumWrote 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/shyftlabs/continuum/agentdb-vector-search)<a href="https://agentmods.dev/skills/shyftlabs/continuum/agentdb-vector-search"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/agentdb-vector-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shyftlabs/continuum/agentdb-vector-search"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/agentdb-vector-search.svg" alt="Reviewed on agentmods" width="80" 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 | $0.02387 |
| Opus 5.5 | $0.00016 | $0.00955 |
| Sonnet 5.5 | $0.00008 | $0.00477 |
| 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 1mo 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 — 0 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.
- 1mo ago First seen · 340 lines · 41 tokens per session scan A beecdac71c19
AgentDB Vector Search is a skill published in the GitHub repository shyftlabs/continuum (84 stars, last pushed 11d ago), licensed Apache-2.0. It adds 41 tokens to every session and 2,387 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to AgentDB Vector Search, differing in 0 lines, and is treated as a copy.
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