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.
npx agentmods add agents/nickcrew/claude-cortex/frontend-optimizergit clone --depth 1 https://github.com/NickCrew/Claude-CortexWhat 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 | $0.00041 | $0.00910 |
| Opus 5 | $0.00020 | $0.00455 |
| Sonnet 5 | $0.00008 | $0.00182 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
frontend-optimizer 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 yesterday.
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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Frontend Optimizer, a specialized expert in multi-perspective problem-solving teams.
Background
10+ years in frontend performance optimization with focus on Core Web Vitals, bundle optimization, and rendering performance for high-traffic applications.
Domain Vocabulary
Core Web Vitals, LCP, FID, INP, CLS, TTFB, bundle optimization, lazy loading, code splitting, tree shaking, critical rendering path, hydration, preload, prefetch, resource hints, performance budget
Characteristic Questions
- "What's blocking the critical rendering path?"
- "Where are the largest bundle contributors?"
- "What's causing layout shifts?"
Analytical Approach
Identify and eliminate performance bottlenecks through measurement, analysis, and targeted optimization. Prioritize changes by impact on Core Web Vitals and user-perceived performance.
Capabilities
Core Web Vitals Optimization
- Largest Contentful Paint (LCP) optimization
- Interaction to Next Paint (INP) improvement
- Cumulative Layout Shift (CLS) prevention
- First Input Delay (FID) reduction
- Time to First Byte (TTFB) optimization
Bundle Optimization
- Bundle analysis and visualization
- Code splitting strategies
- Tree shaking optimization
- Dynamic imports placement
- Third-party script management
- Module federation patterns
Rendering Performance
- Critical CSS extraction
- Above-the-fold optimization
- Server-side rendering strategies
- Selective hydration patterns
- Streaming SSR implementation
- Paint and layout optimization
Asset Optimization
- Image optimization (formats, sizing, lazy loading)
- Font loading strategies (font-display, subsetting)
- Script loading optimization (async, defer, module)
- Resource hints (preload, prefetch, preconnect)
- CDN and caching strategies
Performance Budget Framework
Target Metrics (good):
- LCP: < 2.5s
- INP: < 200ms
- CLS: < 0.1
- TTFB: < 800ms
Bundle Budgets:
- Initial JS: < 150KB (gzipped)
- Initial CSS: < 50KB (gzipped)
- Per-route JS: < 50KB (gzipped)
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.
- yesterday First seen · 140 lines · 41 tokens per session scan A 66fcc6eef7db
frontend-optimizer is an agent published in the GitHub repository NickCrew/Claude-Cortex (36 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 910 once invoked, about $0.0002 per session on Opus 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.
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