agentdb-performance-optimization
Optimize AgentDB Vector Performance
Large AgentDB vector stores can become slow and memory heavy as data grows. This skill gives quantization, HNSW, caching, and batching guidance for faster search and lower memory use.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "agentdb-performance-optimization" from https://skillstore.io/skills/dnyoussef-agentdb-performance-optimization.md and its manifest at https://skillstore.io/api/skills/dnyoussef-agentdb-performance-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.
Test it
Using "agentdb-performance-optimization". I have 500,000 document embeddings and memory usage is too high.
Expected outcome:
- Recommended path: test binary quantization if a small recall loss is acceptable.
- Validation step: compare recall against a representative query set before rollout.
- Operational check: monitor database size, cache hit rate, and average search latency.
Using "agentdb-performance-optimization". Search is slow on a medium AgentDB collection.
Expected outcome:
- Start with scalar quantization, a moderate cache, and balanced HNSW search quality.
- Measure latency before and after each change so improvements are attributable.
- Avoid lowering search quality until recall requirements are clearly defined.
Using "agentdb-performance-optimization". We need a safe benchmark plan for AgentDB optimization.
Expected outcome:
- Use a copied database or staging environment for benchmarks.
- Pin any external package versions before command execution.
- Record hardware, data size, latency, memory, and recall results together.
Security Audit
Medium RiskStatic findings for Ruby backtick execution, hardcoded URLs, and SQLite paths are false positives from Markdown documentation examples. I found one semantic risk: the guide recommends unpinned npx commands that can fetch package code that changes over time. No prompt injection, credential exfiltration, or malicious data-access intent was found.
Confirmed security concerns (1)
Risk Factors
⚙️ External commands (50)
🌐 Network access (2)
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<iframe src="https://skillstore.io/embed/skills/dnyoussef-agentdb-performance-optimization.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
DNYoussef. (2026). agentdb-performance-optimization security audit report (audit version 10) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dnyoussef-agentdb-performance-optimization/audits/10BibTeX citation
@techreport{dnyoussef-dnyoussef-agentdb-performance-optimization-2026,
author = {DNYoussef},
title = {agentdb-performance-optimization security audit report (audit version 10)},
institution = {Skillstore},
year = {2026},
number = {10},
url = {https://skillstore.io/skills/dnyoussef-agentdb-performance-optimization/audits/10},
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-performance-optimization security audit report (audit version 10)"
version: "unspecified"
type: report
authors:
- name: "DNYoussef"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/dnyoussef-agentdb-performance-optimization/audits/10"
identifiers:
- type: other
value: "skillstore:dnyoussef-agentdb-performance-optimization:audit:10"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
ruvnet-agentdb-performance-optimization
2026-08-21
dnyoussef-agentdb-performance-optimization
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Tune Production Vector Search
Choose AgentDB quantization, HNSW, and cache settings for a growing search service.
Reduce Memory Footprint
Plan compression and pruning changes before deploying AgentDB on constrained infrastructure.
Compare Scaling Options
Estimate speed, accuracy, and memory tradeoffs before moving to larger embedding collections.
Try These Prompts
Help me choose an AgentDB quantization mode for my dataset size, memory limit, and accuracy target.
Review my AgentDB search latency goals and recommend HNSW, cache, and retrieval settings to test first.
Create an AgentDB scaling plan for moving from my current vector count to my target count while controlling memory use.
Design a benchmark and risk review for AgentDB performance changes, including recall checks, latency targets, and command safety.
Best Practices
- Benchmark current latency, recall, and memory before changing optimization settings.
- Pin package versions and review commands before running benchmarks in a real workspace.
- Validate accuracy after quantization with representative production queries.
Avoid
- Do not choose binary quantization for accuracy-critical workloads without recall testing.
- Do not increase cache size without checking memory limits.
- Do not run latest-package commands automatically in sensitive environments.
Frequently Asked Questions
Does this skill modify my AgentDB database?
Which AgentDB optimization should I try first?
Can binary quantization reduce accuracy?
Are the benchmark numbers guaranteed?
Is it safe to run the listed npx commands?
Does it support Claude, Codex, and Claude Code?
Developer Details
Author
DNYoussefLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/agentdb-optimizationRef
0519034dad657fb1f7706e0550e962beeda73fdf
Maintenance freshness
7/22/2026
Usage
4 downloads · 270 views
File structure
📄 SKILL.md