whoop-mcp: Skill for Claude Code

.github/skills/performance-optimization/SKILL.md

performance-optimization is a skill for Claude Code, Codex from shashankswe2020-ux/whoop-mcp. It costs 45 tokens per session (2,822 once invoked), scanned A, a copy of performance-optimization, MIT.

A method for making software run faster by measuring it first, finding the actual bottleneck, fixing it, and measuring again. It also covers Core Web Vitals, which are browser metrics for loading speed, responsiveness, and visual stability.

In plain words
What is it for?
Use it when users report slow behavior, monitoring shows regressions, performance requirements exist, Core Web Vitals need improvement, or the application must handle large datasets or high traffic.
Why use it?
It prevents guesswork and avoids adding complexity to parts of an application that are not causing the slowdown.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

This is shashankswe2020-ux/whoop-mcp's own configuration. It tells Claude Code and Codex how to work on whoop-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything whoop-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to shashankswe2020-ux/whoop-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/shashankswe2020-ux/whoop-mcp/main/.github/skills/performance-optimization/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/shashankswe2020-ux/whoop-mcp

Made for: Claude Code, Codex.

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/shashankswe2020-ux/whoop-mcp/performance-optimization/github.svg)](https://agentmods.dev/skills/shashankswe2020-ux/whoop-mcp/performance-optimization)
Your own site
<a href="https://agentmods.dev/skills/shashankswe2020-ux/whoop-mcp/performance-optimization"><img src="https://agentmods.dev/badge/skills/shashankswe2020-ux/whoop-mcp/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/shashankswe2020-ux/whoop-mcp/performance-optimization"><img src="https://agentmods.dev/badge/skills/shashankswe2020-ux/whoop-mcp/performance-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,822 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.
Origin 92% copy Near-identical to another mod 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 $0.00045 $0.02822
Opus 5 $0.00023 $0.01411
Sonnet 5 $0.00009 $0.00564
Haiku 4.5 $0.00005 $0.00282

Measured 11d ago against content hash 46ebf029d5fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, 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 11d 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

This is a copy

92% identical to performance-optimization — 2 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.

.github/skills/performance-optimization/SKILL.md · 351 lines

How it starts

The opening of the file, as written. The whole thing — 351 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, confirm improvement
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 · 351 lines

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. 11d ago First seen · 351 lines · 45 tokens per session scan A 46ebf029d5fe

Subscribe to this mod's changes

performance-optimization is a skill published in the GitHub repository shashankswe2020-ux/whoop-mcp (152 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 2,822 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to performance-optimization, differing in 2 lines, and is treated as a copy.

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