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 skills add rampstackco/claude-skills-starter --skill performance-optimizationgit clone --depth 1 https://github.com/rampstackco/claude-skills-starterWrote 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/rampstackco/claude-skills-starter/performance-optimization)<a href="https://agentmods.dev/skills/rampstackco/claude-skills-starter/performance-optimization"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills-starter/performance-optimization/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/rampstackco/claude-skills-starter/performance-optimization"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills-starter/performance-optimization.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 | $0.00141 | $0.02449 |
| Opus 5.5 | $0.00056 | $0.00980 |
| Sonnet 5.5 | $0.00028 | $0.00490 |
| Haiku 4.5 | $0.00014 | $0.00245 |
Grade A, and why
performance-optimization 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 12d 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
89% identical to performance-optimization — 0 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Diagnose web performance issues and produce a remediation plan. Stack-agnostic. Anchored to Core Web Vitals and standard browser performance patterns.
This skill goes deeper than the performance checks in qa-testing and seo-technical. Use this when performance itself is the goal.
When to use
- Fixing Core Web Vitals (LCP, INP, CLS)
- Diagnosing slow page loads
- Reducing JavaScript bundle size
- Optimizing images, fonts, and other assets
- Fixing layout shift, render-blocking resources, jank
- Pre-launch performance verification
- Annual performance health check
When NOT to use
- General QA after deploys (use
qa-testing) - Technical SEO including indexing and crawling (use
seo-technical) - Code review for general bugs (use
code-review-web)
Required inputs
- The site or page under audit
- Specific performance complaints if any
- Target metrics (Core Web Vitals thresholds, Lighthouse score, custom)
- Browser dev tools access
- Performance monitoring data if available (Real User Monitoring, lab data)
The framework: Core Web Vitals
Three metrics carry most of the weight for both user experience and SEO:
LCP (Largest Contentful Paint)
What it measures: Time until the largest visible content element finishes rendering.
Targets:
- Good: under 2.5 seconds
- Needs improvement: 2.5 to 4.0 seconds
- Poor: over 4.0 seconds
Common causes of poor LCP:
- Slow server response time (TTFB over 800ms)
- Render-blocking JavaScript or CSS
- Large unoptimized images for the LCP element
- Late-loading fonts that delay text render
- Client-side rendering of the LCP element
Common fixes:
- Server-render the LCP element (no client-side render)
- Optimize the LCP image (right format, right size, preloaded)
- Reduce render-blocking resources (defer non-critical CSS and JS)
- Use modern image formats (WebP, AVIF)
- Specify image dimensions to skip layout pass
INP (Interaction to Next Paint)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago Changed · +6 lines 896161a06b87
- 1mo ago First seen · 275 lines · 141 tokens per session scan A 197dff3e07f0
performance-optimization is a skill published in the GitHub repository rampstackco/claude-skills-starter (2 stars, last pushed 12d ago), licensed MIT. It adds 141 tokens to every session and 2,449 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to performance-optimization, differing in 0 lines, and is treated as a copy.
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