Borrowing it
Nothing to install: this file belongs to AkaraChen/aghub. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AkaraChen/aghub/main/.agents/skills/optimize/SKILL.mdgit clone --depth 1 https://github.com/AkaraChen/aghubWrote 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/skills/akarachen/aghub/optimize)<a href="https://agentmods.dev/skills/akarachen/aghub/optimize"><img src="https://agentmods.dev/badge/skills/akarachen/aghub/optimize/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/skills/akarachen/aghub/optimize"><img src="https://agentmods.dev/badge/skills/akarachen/aghub/optimize.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 | $0.00052 | $0.01873 |
| Opus 5.5 | $0.00021 | $0.00749 |
| Sonnet 5.5 | $0.00010 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00187 |
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 1mo 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
92% identical to optimize — 99 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 — 288 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.
- 1mo ago First seen · 288 lines · 52 tokens per session scan A 710eb297e7b7
optimize is a skill published in the GitHub repository AkaraChen/aghub (274 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 1,873 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to optimize, differing in 99 lines, and is treated as a copy.
Other skills, from other repositories
healthcheck
Audit/harden OpenClaw hosts: SSH, firewall, updates, exposure, backups, disk encryption, gateway security.
oma-scholar
Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.
data-charts-tako
Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
apollo-lead-finder
Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against existing contacts by LinkedIn URL.
github-skill
Work with GitHub via the gh CLI — clone repositories, create/list/merge pull requests, create/list issues, and run any other gh command (API calls, workflow runs, releases, repo administration). List operations return parsed JSON.