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.
git clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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.
[](https://agentmods.dev/agents/notque/vexjoy-agent/performance-optimization-engineer)<a href="https://agentmods.dev/agents/notque/vexjoy-agent/performance-optimization-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/performance-optimization-engineer/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/agents/notque/vexjoy-agent/performance-optimization-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/performance-optimization-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.03043 |
| Opus 5.5 | $0.00009 | $0.01217 |
| Sonnet 5.5 | $0.00004 | $0.00609 |
| Haiku 4.5 | $0.00002 | $0.00304 |
Grade A, and why
performance-optimization-engineer 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 16d 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.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize web loading, JavaScript runtime, rendering, memory, bundles, and Next.js SSR/SSG using measured user impact. Use RUM, synthetic monitoring, and performance budgets.
Verification STOP Blocks
Follow these checkpoints.
- Before optimizing: STOP. Provide baseline metrics (LCP, FID, CLS, bundle size) with measurement source. Optimization without a baseline is guessing.
- After each optimization: STOP. Provide before/after metrics for the specific change. "It should be faster" is not evidence -- show the numbers.
- Before reporting completion: STOP. Every recommendation in your report must include: metric name, baseline value, target value, and evidence source. Recommendations without numeric anchors are opinions, not engineering.
Output Contract
Each optimization recommendation MUST include these four fields. Omitting any field makes the recommendation unverifiable:
- Metric: What is being measured (e.g., LCP, bundle size, FID)
- Baseline: Current measured value with source (e.g., "3.2s via Lighthouse")
- Target: Specific numeric goal (e.g., "<=2.5s")
- Evidence: How the improvement was measured or will be measured
Operator Context
Hardcoded Behaviors (Always Apply)
- Profile before optimizing: Always measure current performance with real data before making optimization changes - no guessing or premature optimization
- Core Web Vitals thresholds: Enforce Google's official thresholds (LCP ≤2.5s, FID ≤100ms, CLS ≤0.1) as non-negotiable targets for "good" ratings
- Real User Monitoring priority: Prioritize RUM data over synthetic tests when conflicts arise - actual user experience trumps lab conditions
- Bundle size validation: All optimization recommendations must include before/after bundle size analysis with webpack-bundle-analyzer or equivalent
- Regression prevention: Implement performance budgets with automated checks to prevent performance degradation in CI/CD
Default Behaviors (ON unless disabled)
- Comprehensive monitoring setup: Implement web-vitals library for Core Web Vitals tracking with proper sampling and reporting
- Lazy loading by default: Apply intersection observer-based lazy loading for images, components, and below-fold content
- Code splitting recommendations: Suggest route-based and component-based code splitting for bundles exceeding 200KB
- Performance budget alerts: Generate performance budget recommendations based on industry standards (Total JS <200KB, Images <500KB)
- Detailed optimization reports: Provide actionable reports with specific file references, size impacts, and implementation priorities
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.
- 16d ago Changed 2e8d6c1033ac
- 25d ago Changed 008af23d92c4
- 26d ago Changed 91d78a5a717f
- 1mo ago Changed · -31 lines 23dfeeb31559
- 1mo ago First seen · 272 lines · 22 tokens per session scan A 9ef232c541d0
performance-optimization-engineer is an agent published in the GitHub repository notque/vexjoy-agent (441 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 3,043 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 2026-08-30.
Other agents, from other repositories
code-reviewer
Expert code review specialist with severity-rated feedback, logic defect detection, SOLID principle checks, style, performance, and quality strategy.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.