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 skills/qinye6/pi-ccg/optimizenpx skills add qinye6/pi-ccg --skill optimizegit clone --depth 1 https://github.com/qinye6/pi-ccgWhat 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 | $0.00052 | $0.01877 |
| Opus 5 | $0.00026 | $0.00938 |
| Sonnet 5 | $0.00010 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
optimize 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 3d 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.
This is a copy
86% identical to optimize — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identify and fix performance issues to create faster, smoother user experiences.
Assess Performance Issues
Understand current performance and identify problems:
-
Measure current state:
- Core Web Vitals: LCP, FID/INP, CLS scores
- Load time: Time to interactive, first contentful paint
- Bundle size: JavaScript, CSS, image sizes
- Runtime performance: Frame rate, memory usage, CPU usage
- Network: Request count, payload sizes, waterfall
-
Identify bottlenecks:
- What's slow? (Initial load? Interactions? Animations?)
- What's causing it? (Large images? Expensive JavaScript? Layout thrashing?)
- How bad is it? (Perceivable? Annoying? Blocking?)
- Who's affected? (All users? Mobile only? Slow connections?)
CRITICAL: Measure before and after. Premature optimization wastes time. Optimize what actually matters.
Optimization Strategy
Create systematic improvement plan:
Loading Performance
Optimize Images:
- Use modern formats (WebP, AVIF)
- Proper sizing (don't load 3000px image for 300px display)
- Lazy loading for below-fold images
- Responsive images (
srcset,pictureelement) - Compress images (80-85% quality is usually imperceptible)
- Use CDN for faster delivery
<img
src="hero.webp"
srcset="hero-400.webp 400w, hero-800.webp 800w, hero-1200.webp 1200w"
sizes="(max-width: 400px) 400px, (max-width: 800px) 800px, 1200px"
loading="lazy"
alt="Hero image"
/>
Reduce JavaScript Bundle:
- Code splitting (route-based, component-based)
- Tree shaking (remove unused code)
- Remove unused dependencies
- Lazy load non-critical code
- Use dynamic imports for large components
// Lazy load heavy component
const HeavyChart = lazy(() => import('./HeavyChart'));
Optimize CSS:
- Remove unused CSS
- Critical CSS inline, rest async
- Minimize CSS files
- Use CSS containment for independent regions
Optimize Fonts:
- Use
font-display: swaporoptional - Subset fonts (only characters you need)
- Preload critical fonts
- Use system fonts when appropriate
- Limit font weights loaded
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.
- 3d ago First seen · 267 lines · 52 tokens per session scan A c6dd784b70bd
optimize is a skill published in the GitHub repository qinye6/pi-ccg (10 stars, last pushed 8d ago), licensed MIT. It adds 52 tokens to every session and 1,877 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to optimize, differing in 11 lines, and is treated as a copy.
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