AgentDB Vector Search

AgentDB Vector Search is a skill for Claude Code from avariza/solo-agent-vs-hive-mind. It costs 41 tokens per session (2,387 once invoked), scanned A, original, no licence file.

Vector search with AgentDB for finding documents or records by meaning rather than exact words. It supports retrieval for RAG, a method where an AI answers using relevant external information.

In plain words
What is it for?
Building document retrieval, similarity matching, semantic search, and knowledge-base systems.
Why use it?
Keyword search can miss useful results when the wording differs. Semantic search finds similar content and supplies relevant context to an AI system.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Building document retrieval, similarity matching, semantic search, and knowledge-base systems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search
Install

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.

Any agent
npx skills add avariza/solo-agent-vs-hive-mind --skill agentdb-vector-search
Clone the repo
git clone --depth 1 https://github.com/avariza/solo-agent-vs-hive-mind

Made for: Claude Code.

Wrote 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.

agentmods badge for AgentDB Vector Search

README.md
[![agentmods](https://agentmods.dev/badge/skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search/github.svg)](https://agentmods.dev/skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search)
Your own site
<a href="https://agentmods.dev/skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search"><img src="https://agentmods.dev/badge/skills/avariza/solo-agent-vs-hive-mind/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.

agentmods 80×15 button for AgentDB Vector Search

Your own site · 80×15
<a href="https://agentmods.dev/skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search"><img src="https://agentmods.dev/badge/skills/avariza/solo-agent-vs-hive-mind/agentdb-vector-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,387 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00041 $0.02387
Opus 5 $0.00020 $0.01193
Sonnet 5 $0.00008 $0.00477
Haiku 4.5 $0.00004 $0.00239

Measured 7d ago against content hash beecdac71c19, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 7d 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.

.claude/skills/agentdb-vector-search/SKILL.md · 340 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 7d ago First seen · 340 lines · 41 tokens per session scan A beecdac71c19

Subscribe to this mod's changes

AgentDB Vector Search is a skill published in the GitHub repository avariza/solo-agent-vs-hive-mind (5 stars, last pushed 4mo ago), with no licence file. It adds 41 tokens to every session and 2,387 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

9router-embeddings

Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

decolua/9router · 66 tokens

azure-search-documents-dotnet

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…

microsoft/skills · 102 tokens

browserwing-admin

Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.

MemTensor/MemOS · 47 tokens

embedding-strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

foryourhealth111-pixel/Vibe-Skills · 37 tokens