Skills agentdb-semantic-vector-search
📦

agentdb-semantic-vector-search

v1.0.0 Content revision r1 Safe ⚙️ External commands🌐 Network access

Build AgentDB Semantic Search

Teams need relevant document retrieval for RAG and knowledge bases. This skill guides AgentDB setup, embedding storage, indexing, API search, and relevance tuning.

Supports: Claude Codex Code(CC)
🥈 80 Silver

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-semantic-vector-search" from https://skillstore.io/skills/dnyoussef-agentdb-semantic-vector-search.md and its manifest at https://skillstore.io/api/skills/dnyoussef-agentdb-semantic-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-semantic-vector-search.md Signed manifest GET /api/skills/dnyoussef-agentdb-semantic-vector-search/manifest Signed lockfile GET /api/skills/dnyoussef-agentdb-semantic-vector-search/lockfile

Test it

Using "agentdb-semantic-vector-search". Plan semantic search for support articles.

Expected outcome:

  • A five-phase implementation checklist for database setup, ingestion, indexing, API design, and tuning.
  • Recommended metadata fields for category, title, source, and freshness.
  • Success metrics for latency, top result quality, and retrieval accuracy.

Using "agentdb-semantic-vector-search". Improve weak search results in a knowledge base.

Expected outcome:

  • A tuning plan that compares vector retrieval, keyword matching, and hybrid weighting.
  • A re-ranking step for improving final result order.
  • A validation plan using sample queries and expected relevant documents.

Using "agentdb-semantic-vector-search". Design a RAG retrieval API.

Expected outcome:

  • Endpoint responsibilities for search, filtering, and context retrieval.
  • Guidance for top result limits, metadata filters, and response fields.
  • Testing criteria for relevance, latency, and reliability.

Security Audit

Safe
v8 • 7/6/2026 Open versioned report

The flagged reconnaissance items are false positives caused by normal hybrid search documentation. The external command finding is a Markdown TypeScript code fence, and the network finding is a documentation URL. No semantic prompt injection, data exfiltration intent, or malicious behavior was found in the reviewed files.

4
Files scanned
277
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:79-104
🌐 Network access (1)
SKILL.md:124
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
Security Assessment

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https://skillstore.io/skills/dnyoussef-agentdb-semantic-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-semantic-vector-search security audit report (audit version 8) [Author version 1.0.0]. Skillstore. https://skillstore.io/skills/dnyoussef-agentdb-semantic-vector-search/audits/8

BibTeX citation

@techreport{dnyoussef-dnyoussef-agentdb-semantic-vector-search-2026, author = {DNYoussef}, title = {agentdb-semantic-vector-search security audit report (audit version 8)}, institution = {Skillstore}, year = {2026}, number = {8}, url = {https://skillstore.io/skills/dnyoussef-agentdb-semantic-vector-search/audits/8}, note = {Author version 1.0.0} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "agentdb-semantic-vector-search security audit report (audit version 8)" version: "1.0.0" type: report authors: - name: "DNYoussef" date-released: "2026-07-06" url: "https://skillstore.io/skills/dnyoussef-agentdb-semantic-vector-search/audits/8" identifiers: - type: other value: "skillstore:dnyoussef-agentdb-semantic-vector-search:audit:8" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
100
Maintainability
85
Content
70
Community
83
Spec Compliance

What You Can Build

Build RAG Retrieval Backend

Create a vector search layer that returns relevant context for LLM responses.

Improve Knowledge Base Search

Add semantic matching so users can find documents by meaning, not exact keywords.

Evaluate Search Relevance

Plan retrieval metrics, top result checks, and tuning cycles for search quality.

Try These Prompts

Plan a Basic Index 
Use this skill to plan an AgentDB semantic search index for my document corpus. Include dimensions, metadata fields, and success metrics.
Design Document Ingestion 
Use this skill to design a document ingestion flow with embeddings, metadata storage, and duplicate handling for AgentDB.
Create Search API Plan 
Use this skill to outline REST API endpoints for semantic search, filtering, pagination, and result ranking.
Optimize Hybrid Retrieval 
Use this skill to improve an existing AgentDB search system with hybrid retrieval, re-ranking, and relevance evaluation.

Best Practices

  • Validate embedding dimensions before storing vectors.
  • Keep metadata filters simple and aligned with user search needs.
  • Measure relevance with representative queries before production rollout.

Avoid

  • Do not assume keyword search alone will satisfy semantic retrieval goals.
  • Do not store document text without clear privacy and retention controls.
  • Do not tune latency without checking result relevance.

Frequently Asked Questions

What does this skill help me build?
It helps build semantic vector search systems with AgentDB for documents, RAG, and knowledge bases.
Does it include a full production service?
No. It provides workflow guidance and examples that need production hardening.
Which users benefit from this skill?
Backend engineers, ML engineers, and product teams building intelligent search can benefit.
Does it support hybrid search?
Yes. The skill describes combining vector search with keyword matching and re-ranking.
Do I need an embedding model?
Yes. The workflow depends on embeddings from a compatible model or service.
Can I use it with Claude, Codex, and Claude Code?
Yes. The marketplace metadata lists support for Claude, Codex, and Claude Code.

Developer Details

Author

DNYoussef

License

MIT

Author version

v1.0.0

Skillstore revision

r1

Repository

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

Ref

72d5025b022c77f7a51bdf5c1637c689c80e89d1

Maintenance freshness

7/18/2026

Usage

5 downloads · 220 views

File structure

📄 process-diagram.gv

📄 PROCESS.md

📄 README.md

📄 SKILL.md