Skills agentdb-vector-search

📦

agentdb-vector-search

Name: agentdb-vector-search
Author: DNYoussef

Content revision r1 Safe ⚙️ External commands🌐 Network access

Build AgentDB Vector Search

Teams need fast semantic retrieval for documents, RAG systems, and knowledge bases. This skill gives AgentDB setup, search patterns, and tuning guidance.

Supports: Claude Codex Code(CC)

🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "agentdb-vector-search" from https://skillstore.io/skills/dnyoussef-agentdb-vector-search.md and its manifest at https://skillstore.io/api/skills/dnyoussef-agentdb-vector-search/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/dnyoussef-agentdb-vector-search.md Signed manifest GET /api/skills/dnyoussef-agentdb-vector-search/manifest Signed lockfile GET /api/skills/dnyoussef-agentdb-vector-search/lockfile

Test it

Using "agentdb-vector-search". I need semantic search for technical support articles.

Expected outcome:

Recommended setup: use one embedding model, match the database dimension, and store article text with source metadata.
Retrieval plan: query top results with a minimum score, then filter by product, version, and article status.
Quality check: inspect low-score matches and adjust thresholds before connecting the results to generation.

Using "agentdb-vector-search". My vector search is slow on a large collection.

Expected outcome:

Review index status, batch import behavior, cache settings, and whether quantization is enabled.
Measure latency with representative queries before changing distance metrics or thresholds.
Use metadata filters to reduce candidate sets when users already provide product or time constraints.

Using "agentdb-vector-search". I want Claude Code to query AgentDB through MCP.

Expected outcome:

Install and review the AgentDB MCP server command, then add it to Claude Code only after package verification.
Confirm the available tools for querying, storing documents, and checking database statistics.
Test with non-sensitive sample documents before connecting production data.

Security Audit

Safe

v8 • 7/5/2026 Open versioned report

The reviewed SKILL.md is documentation for AgentDB vector search and did not show prompt injection, data exfiltration, or malicious intent. Static findings were false positives from Markdown code fences, local database examples, and documentation links.

Files scanned

340

Lines analyzed

Review items

False positives ignored

Risk Factors

⚙️ External commands (43)

🌐 Network access (2)

SKILL.md:334 SKILL.md:337

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Open versioned report

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https://skillstore.io/skills/dnyoussef-agentdb-vector-search/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

DNYoussef. (2026). agentdb-vector-search security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dnyoussef-agentdb-vector-search/audits/8

BibTeX citation

@techreport{dnyoussef-dnyoussef-agentdb-vector-search-2026,
  author = {DNYoussef},
  title = {agentdb-vector-search security audit report (audit version 8)},
  institution = {Skillstore},
  year = {2026},
  number = {8},
  url = {https://skillstore.io/skills/dnyoussef-agentdb-vector-search/audits/8},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "agentdb-vector-search security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
  - name: "DNYoussef"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/dnyoussef-agentdb-vector-search/audits/8"
identifiers:
  - type: other
    value: "skillstore:dnyoussef-agentdb-vector-search:audit:8"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

DNYoussef Recommended Current

dnyoussef-agentdb-vector-search

Skillstore Score 78

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

ruvnet

ruvnet-agentdb-vector-search

Skillstore Score 75

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Build a RAG Knowledge Base

Use AgentDB to store document embeddings, retrieve relevant context, and support answer generation.

Add Semantic Product Search

Combine vector similarity with metadata filters for product, content, or support search experiences.

Tune Vector Retrieval Settings

Compare distance metrics, thresholds, quantization choices, and batch operations for retrieval workloads.

Try These Prompts

Start a Local Vector Database

Use agentdb-vector-search to set up a local AgentDB vector database for [embedding model] with [dimension] dimensions. Show the safest CLI steps and explain each setting.

Add Similarity Search

Use agentdb-vector-search to design a similarity search flow for [document type]. Include storage fields, query options, score thresholds, and result handling.

Build a RAG Retrieval Flow

Use agentdb-vector-search to create a RAG retrieval plan for [knowledge base]. Include embedding, storage, retrieval, diversity, and context assembly steps.

Tune Hybrid Search at Scale

Use agentdb-vector-search to tune an AgentDB index for [scale] vectors. Recommend metric choices, quantization, metadata filters, cache settings, and measurement steps.

Best Practices

Pin package versions and review third-party CLI packages before running install or MCP commands.
Match vector dimensions to the embedding model before importing or querying data.
Use metadata filters and score thresholds to reduce irrelevant retrieval results.

Avoid

Running npx commands from untrusted sources without package review.
Mixing embeddings from different models inside one vector index.
Treating benchmark figures as guaranteed production performance.

Frequently Asked Questions

What does this skill help build?

It helps build AgentDB-based semantic search, similarity retrieval, and RAG context retrieval workflows.

Does it include a complete application?

No. It provides setup guidance, command examples, API patterns, and tuning advice.

Do I need an embedding provider?

Yes. You need OpenAI embeddings or another model that produces vectors with known dimensions.

Can it work with Claude Code?

Yes. The skill includes AgentDB MCP setup guidance for Claude Code workflows.

Is the database file sensitive?

It can be. Stored vectors, metadata, and exports may reveal source document content.

How should I choose vector dimensions?

Use the dimension required by your embedding model and keep it consistent across stored vectors.

Developer Details

Author

DNYoussef

License

MIT

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/agentdb-vector-search

Ref

c579efd02184f1128d6741d16e6c8d951d7d4208

Maintenance freshness

7/18/2026

Usage

5 downloads · 248 views

File structure

📄 SKILL.md