advanced-agentdb-vector-search-implementation

advanced-agentdb-vector-search-implementation is a skill for Claude Code from DNYoussef/context-cascade. It costs 21 tokens per session (8,279 once invoked), scanned A, original, MIT.

A guide to advanced AgentDB vector search, where results are found by comparing the meaning of stored information rather than exact words. It is intended for distributed AI systems, shared memory between agents, and advanced search applications.

In plain words
What is it for?
Use it to implement vector search, shared memory for multiple agents, and distributed information-retrieval systems.
Why use it?
It helps teams design searches that can find related information even when the wording differs.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the TodoWrite tool.

Good fit Use it to implement vector search, shared memory for multiple agents, and distributed information-retrieval systems.

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Install with agentmods
npx agentmods add skills/dnyoussef/context-cascade/advanced-agentdb-vector-search-implementation
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 DNYoussef/context-cascade --skill advanced-agentdb-vector-search-implementation
Clone the repo
git clone --depth 1 https://github.com/DNYoussef/context-cascade

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.

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README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,279 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 original 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 $0.00021 $0.08279
Opus 5.5 $0.00008 $0.03312
Sonnet 5.5 $0.00004 $0.01656
Haiku 4.5 $0.00002 $0.00828

Measured 1mo ago against content hash 6e2223030e29, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

advanced-agentdb-vector-search-implementation 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.

skills/platforms/advanced-agentdb-vector-search-implementation/SKILL.md · 1,168 lines

How it starts

The opening of the file, as written. The whole thing — 1,168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Advanced AgentDB Vector Search Implementation


LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

  • Location: .claude/library/catalog.json
  • If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

  • Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
  • If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

  • Location: D:\Projects\*
  • If found: EXTRACT and adapt

Decision Matrix

Match Action
Library >90% REUSE directly
Library 70-90% ADAPT minimally
Pattern exists FOLLOW pattern
In project EXTRACT
No match BUILD (add to library after)

Overview

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration for building distributed AI systems, multi-agent coordination, and advanced vector search applications.

When to Use This Skill

Use this skill when you need to:

  • Build distributed vector search systems
  • Implement multi-agent coordination with shared memory
  • Create custom similarity metrics for specialized domains
  • Deploy hybrid search combining vector and traditional methods
  • Scale AgentDB to production with high availability
  • Synchronize multiple AgentDB instances in real-time

SOP Framework: 5-Phase Advanced Vector Search Deployment

Phase 1: Setup AgentDB Infrastructure (2-3 hours)

Objective: Initialize multi-database AgentDB infrastructure with proper configuration

Agent: backend-dev

Steps:

  1. Install AgentDB with advanced features
npm install agentdb-advanced@latest
npm install @agentdb/quic-sync @agentdb/distributed
  1. Initialize primary database
import { AgentDB } from 'agentdb-advanced';
import { QUICSync } from '@agentdb/quic-sync';

const primaryDB = new AgentDB({
  name: 'primary-vector-db',
  dimensions: 1536, // OpenAI embedding size
  indexType: 'hnsw',
  distanceMetric: 'cosine',
  persistPath: './data/primary',
  advanced: {
    enableQUIC: true,
    multiDB: true,
    hybridSearch: true
  }
});

await primaryDB.initialize();

Read the full file on GitHub · 1,168 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 1,168 lines · 0 tokens per session scan A 6e2223030e29

Subscribe to this mod's changes

advanced-agentdb-vector-search-implementation is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 8,279 once invoked, about $0.0001 per session on Opus 5.5. 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.