optimize

Establish baselines, fix bottlenecks, and report before/after performance.

24|3|Updated Feb 22, 2026

Search Tags:#code quality #performance #optimization #profiling #bottleneck #gpu #ml

Ready to run

npx skills add https://github.com/Borda/.home --skill optimize-borda

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: optimize
Source: https://github.com/Borda/.home/tree/main/.claude/skills/optimize
Command: npx skills add https://github.com/Borda/.home --skill optimize-borda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses slow-running code by systematically identifying and fixing performance bottlenecks, ensuring your applications run efficiently.

Core Features & Use Cases

Baseline Measurement: Establishes a clear performance benchmark before any changes are made.
Bottleneck Identification: Pinpoints the single biggest performance issue using specialized agents.
Targeted Optimization: Implements specific fixes for identified bottlenecks across CPU, memory, I/O, and ML/GPU workloads.
Verification & Reporting: Measures improvements and provides a clear before/after performance report.
Use Case: Optimize a slow Python script by having the skill identify the CPU-intensive function, implement a more efficient algorithm, and confirm a significant speedup.

Quick Start

Use the optimize skill to find and fix performance issues in the 'data_processing.py' module.

Frequently Asked Questions about optimize

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

FAQPage Schema

How do I find and fix bottlenecks in a slow Python script?▼

To fix bottlenecks in a slow Python script, this skill establishes a performance baseline, uses a specialized agent to pinpoint the biggest issue, and iteratively implements targeted fixes across CPU, memory, or I/O before generating a before/after report.

What is the best way to profile ML and GPU workloads for performance issues?▼

Profiling ML and GPU workloads is handled by a specialized perf-optimizer agent that identifies specific bottlenecks and applies targeted optimizations, measuring improvements against an initial baseline.

Can I use this to optimize code across different areas like memory and concurrency?▼

Yes, this skill implements targeted optimizations across CPU, memory, I/O, concurrency, and ML/GPU workloads after identifying the single biggest performance issue in your application.

Do I need Python profiling tools to run the optimization workflow?▼

Yes, Python profiling tools and agentic execution are required to orchestrate the deep-dive performance analysis, establish baselines, and iteratively improve your code.

How does this approach verify that the targeted code optimization actually worked?▼

The optimization process verifies improvements by measuring the performance changes after implementing fixes and producing a clear before/after performance report for comparison.