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4asaanAI
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performance-review

Analyze code and system performance to prioritize bottlenecks and optimizations.

Updated Aug 27, 2026
Search Tags:#performance#react#optimization#llm#supabase#profiling#bottleneck
One-click install
npx skills add https://github.com/4asaanAI/Claude-patches --skill performance-review-4asaanai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-review
Source: https://github.com/4asaanAI/Claude-patches/tree/main/framework-foundry/Claude%20Plugins/layaa-ai/skills/performance-review
Command: npx skills add https://github.com/4asaanAI/Claude-patches --skill performance-review-4asaanai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams identify, prioritize, and fix performance bottlenecks across code, database queries, rendering, network, and LLM usage so applications run faster and cost less.

Core Features & Use Cases

  • Baseline measurement: Establish current performance metrics and target goals before changes.
  • Profiling and diagnosis: Identify where time and resources are spent (network, rendering, DB, LLM) and isolate the primary bottleneck.
  • Targeted optimization: Recommend focused fixes such as React rendering improvements, Supabase query tuning, bundle splitting, and LLM prompt/token efficiency.
  • Verification and regression checks: Re-measure after changes and ensure no functional regressions are introduced.
  • Use Case: Speed up a slow React view by profiling renders, reducing unnecessary queries, splitting heavy bundles, and trimming LLM prompts to lower latency and cost.

Quick Start

Analyze this React app's slow page, identify the single biggest bottleneck with profiling data, and provide prioritized fixes plus measurable before-and-after metrics.

Frequently Asked Questions about performance-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find and fix React rendering bottlenecks?▼

Analyze profiling outputs and code snippets to isolate unnecessary renders, apply targeted optimizations like bundle splitting, and verify improvements with measurable before-and-after metrics.

What is the best way to optimize slow Supabase queries in my web app?▼

Optimizing slow Supabase queries involves profiling database metrics to isolate primary bottlenecks, applying targeted query tuning recommendations, and running regression checks to ensure no functional regressions.

How can I reduce LLM token usage and latency during profiling?▼

Reduce LLM token usage by analyzing performance metrics to identify prompt inefficiencies, applying targeted token efficiency recommendations, and verifying latency improvements through baseline measurement and regression checks.

Can I use this to establish baseline performance metrics before optimizing bundle size?▼

Yes, you can establish current performance metrics and target goals before changes, then apply targeted bundle splitting recommendations and re-measure to ensure measurable improvements without regressions.

What kind of profiling data do I need to diagnose system performance bottlenecks?▼

You can provide profiling outputs, code snippets, or performance metrics from your web applications and services to isolate the primary bottleneck across network, rendering, database, or LLM layers.

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