performance-optimization

performance-optimization is a skill for Claude Code from addyosmani/agent-skills. It costs 59 tokens per session (3,804 once invoked), scanned A, original, MIT.

A measurement-led method for making software faster across the interface, server, database, or data queries. It starts by finding the actual bottleneck before changing code.

In plain words
What is it for?
Use it when load times or response times are too slow, performance has regressed, Core Web Vitals are poor, or profiling shows a bottleneck.
Why use it?
It prevents time being spent on changes that do not improve real performance and avoids adding unnecessary complexity. Repeating measurements shows whether a fix worked.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agent-skills plugin — 25 skills, 10 commands, 4 agents shipped together

Good fit Use it when load times or response times are too slow, performance has regressed, Core Web Vitals are poor, or profiling shows a bottleneck.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/addyosmani/agent-skills/performance-optimization
About the project

addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.

addyosmani/agent-skills · 107,042 stars · on GitHub · skills.addy.ie

Install

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.

Any agent
npx skills add addyosmani/agent-skills --skill performance-optimization
Clone the repo
git clone --depth 1 https://github.com/addyosmani/agent-skills

Made for: Claude Code.

Or install agent-skills, the plugin that ships this one along with the rest of its 25 skills, 10 commands, 4 agents.

Wrote 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.

agentmods badge for performance-optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/addyosmani/agent-skills/performance-optimization/github.svg)](https://agentmods.dev/skills/addyosmani/agent-skills/performance-optimization)
Your own site
<a href="https://agentmods.dev/skills/addyosmani/agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/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.

agentmods 80×15 button for performance-optimization

Your own site · 80×15
<a href="https://agentmods.dev/skills/addyosmani/agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/performance-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,804 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 14 Oct 2026
  • Snyk pass 14 Oct 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium MCP Rug Pull · line 435
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 438
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00059 $0.03804
Opus 5.5 $0.00024 $0.01522
Sonnet 5.5 $0.00012 $0.00761
Haiku 4.5 $0.00006 $0.00380

Measured 2d ago against content hash bd36a30e55a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/performance-optimization/SKILL.md · 268 lines

How it starts

The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Optimization

Overview

Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters.

When to Use

  • Performance requirements exist in the spec (load time budgets, response time SLAs)
  • Users or monitoring report slow behavior
  • Core Web Vitals scores are below thresholds
  • You suspect a change introduced a regression
  • Building features that handle large datasets or high traffic

When NOT to use: Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains.

Core Web Vitals Targets

Metric Good Needs Improvement Poor
LCP (Largest Contentful Paint) ≤ 2.5s ≤ 4.0s > 4.0s
INP (Interaction to Next Paint) ≤ 200ms ≤ 500ms > 500ms
CLS (Cumulative Layout Shift) ≤ 0.1 ≤ 0.25 > 0.25

The Optimization Workflow

1. MEASURE  → Establish baseline with real data
2. IDENTIFY → Find the actual bottleneck (not assumed)
3. FIX      → Address the specific bottleneck
4. VERIFY   → Measure again; keep or revert
5. GUARD    → Add monitoring or tests to prevent regression

Step 1: Measure

Two complementary approaches — use both:

  • Synthetic (Lighthouse, DevTools Performance tab): Controlled conditions, reproducible. Best for CI regression detection and isolating specific issues.
  • RUM (web-vitals library, CrUX): Real user data in real conditions. Required to validate that a fix actually improved user experience.

Frontend:

// Synthetic: Lighthouse in Chrome DevTools (or CI)
// Chrome DevTools → Performance tab → Record
// Chrome DevTools MCP → Performance trace

// RUM: Web Vitals library in code
import { onLCP, onINP, onCLS } from 'web-vitals';

onLCP(console.log);
onINP(console.log);
onCLS(console.log);

Read the full file on GitHub · 268 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 2d ago Changed bd36a30e55a5
  2. 12d ago Changed · -229 lines 665a83a7218d
  3. 1mo ago First seen · 497 lines · 59 tokens per session scan A 00694d0c69bb

Subscribe to this mod's changes

performance-optimization is a skill published in the GitHub repository addyosmani/agent-skills (107,042 stars, last pushed 2d ago), licensed MIT. It adds 59 tokens to every session and 3,804 once invoked, about $0.0002 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.

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