Performance Expert

This is the performance toolkit you reach for when things are slow and you need to figure out why. It covers profiling with cProfile, fixing N+1 queries with eager loading, setting up proper database connection pooling, and frontend patterns like debouncing and virtual scrolling. The examples are practical: memoization with lru_cache, batch database operations, Redis caching strategies, and switching from Python loops to NumPy vectorization. It's language-agnostic in scope but Python-heavy in implementation. Good for when you're past the "just throw hardware at it" phase and need to actually measure and optimize. The breadth is both its strength and weakness, covering everything from algorithm optimization to infrastructure tuning without going too deep on any single area.

skills.sh installs: 196
GitHub stars: 44
last updated: Aug 21, 2026

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Install

$ npx -y skills add personamanagmentlayer/pcl --skill performance-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

Performance Expert

Expert guidance for performance optimization, profiling, benchmarking, and system tuning.

Core Concepts

Performance Fundamentals

Response time vs throughput
Latency vs bandwidth
CPU, memory, I/O bottlenecks
Concurrency vs parallelism
Caching strategies
Load balancing

Optimization Areas

Algorithm optimization
Database optimization
Network optimization
Frontend performance
Backend performance
Infrastructure tuning

Profiling Tools

CPU profilers
Memory profilers
Network profilers
Application Performance Monitoring (APM)
Load testing tools

Python Performance

import cProfile
import pstats
import timeit
import memory_profiler
from functools import lru_cache
from typing import List
import numpy as np

# Performance Profiling
def profile_function(func):
    """Decorator for profiling function execution"""
    def wrapper(*args, **kwargs):
        profiler = cProfile.Profile()
        profiler.enable()

        result = func(*args, **kwargs)

        profiler.disable()
        stats = pstats.Stats(profiler)
        stats.sort_stats('cumulative')
        stats.print_stats(10)  # Top 10 functions

SKILL.md on GitHub