Skills agentdb-vector-search-optimization
📦

agentdb-vector-search-optimization

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

Optimize AgentDB Vector Search

Large vector databases can become slow, memory-heavy, and hard to benchmark. This skill provides a structured AgentDB optimization workflow for quantization, HNSW indexing, caching, and validation.

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

Test it

Using "agentdb-vector-search-optimization". A team needs lower memory use for a 1536-dimension embedding index.

Expected outcome:

Recommended plan: capture baseline memory, apply product quantization at 4x compression, train codebooks, then compare recall and p95 latency.

Using "agentdb-vector-search-optimization". Search latency is too high for frequent nearest-neighbor queries.

Expected outcome:

Recommended plan: build an HNSW index, tune construction and search parameters, then benchmark speedup against the original exact search path.

Using "agentdb-vector-search-optimization". Repeated queries are creating unnecessary database load.

Expected outcome:

Recommended plan: enable an LRU query cache, set a time-based expiration policy, monitor hit rate, and resize the cache from observed traffic.

Security Audit

Safe
v8 • 7/5/2026 Open versioned report

The two static alerts were reviewed against SKILL.md. The TypeScript example at lines 80-103 is documentation, not shell execution, and the hardcoded URL at line 132 is a resource link rather than active network behavior. No prompt injection or malicious intent was found in the reviewed skill files.

4
Files scanned
281
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:80-103
🌐 Network access (1)
SKILL.md:132
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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Academic citations (APA · BibTeX · CFF)

APA citation

DNYoussef. (2026). agentdb-vector-search-optimization security audit report (audit version 8) [Author version 1.0.0]. Skillstore. https://skillstore.io/skills/dnyoussef-agentdb-vector-search-optimization/audits/8

BibTeX citation

@techreport{dnyoussef-dnyoussef-agentdb-vector-search-optimization-2026, author = {DNYoussef}, title = {agentdb-vector-search-optimization security audit report (audit version 8)}, institution = {Skillstore}, year = {2026}, number = {8}, url = {https://skillstore.io/skills/dnyoussef-agentdb-vector-search-optimization/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-vector-search-optimization security audit report (audit version 8)" version: "1.0.0" type: report authors: - name: "DNYoussef" date-released: "2026-07-05" url: "https://skillstore.io/skills/dnyoussef-agentdb-vector-search-optimization/audits/8" identifiers: - type: other value: "skillstore:dnyoussef-agentdb-vector-search-optimization:audit:8" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
100
Maintainability
87
Content
70
Community
83
Spec Compliance

What You Can Build

Reduce Memory Footprint

Tune quantization settings for a large embedding store while preserving search accuracy.

Improve Search Latency

Add HNSW indexing and benchmark p95 latency for high-volume vector queries.

Plan Optimization Work

Estimate phases, duration, metrics, and validation steps for an AgentDB performance project.

Try These Prompts

Measure Baseline 
Help me measure baseline AgentDB vector search performance. Include latency, memory, throughput, accuracy, and cache metrics.
Choose Quantization 
Recommend a quantization strategy for my AgentDB vectors. I need lower memory use while keeping search accuracy above 95 percent.
Tune HNSW Search 
Create an HNSW tuning plan for AgentDB. Include M, efConstruction, efSearch, expected tradeoffs, and benchmark steps.
Design Full Optimization 
Design an end-to-end AgentDB optimization plan for millions of vectors. Include baselines, quantization, indexing, caching, batch operations, and acceptance criteria.

Best Practices

  • Measure baseline performance before changing quantization, indexing, or caching.
  • Validate accuracy after every compression or approximate search change.
  • Benchmark p95 latency and throughput with production-like vector distributions.

Avoid

  • Applying maximum compression before checking recall impact.
  • Reporting speedup without baseline, dataset size, and hardware context.
  • Tuning cache size without measuring hit rate and eviction behavior.

Frequently Asked Questions

What problem does this skill solve?
It helps optimize AgentDB vector search for lower memory use, faster search, and measurable performance gains.
Does it run the optimization automatically?
No. It provides guidance, example code, and validation steps for engineers to apply in their environment.
Which optimization methods are covered?
It covers quantization, HNSW indexing, query caching, embedding caching, batch operations, and benchmark comparison.
Can it help preserve search accuracy?
Yes. The workflow includes accuracy checks after compression and indexing changes.
Who should use this skill?
It is useful for machine learning engineers, backend engineers, and technical leads managing vector search performance.
What should I prepare before using it?
Prepare baseline metrics, representative query sets, dataset size, vector dimensions, and target latency or memory goals.

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-optimizing-vector-search-use-agentdb-optimization

Ref

c579efd02184f1128d6741d16e6c8d951d7d4208

Maintenance freshness

7/20/2026

Usage

3 downloads · 213 views

File structure

📄 process-diagram.gv

📄 PROCESS.md

📄 README.md

📄 SKILL.md