performance-reference

Profile Haiku, Sonnet, and Opus models for performance optimization workflows.

1|Updated Feb 21, 2026
Ready to run
npx skills add https://github.com/kssumin/claude-playground --skill performance-reference
πŸ’¬ Prompt for ChatGPT / Claude Chatβ–Ό
Please help me install this Agent Skill.
Skill: performance-reference
Source: https://github.com/kssumin/claude-playground/tree/main/.claude/skills/performance-reference
Command: npx skills add https://github.com/kssumin/claude-playground --skill performance-reference

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

μ„±λŠ₯ μ΅œμ ν™” 상세 레퍼런슀둜 λͺ¨λΈλ³„ νŠΉμ„±(Haiku/Sonnet/Opus), μ»¨ν…μŠ€νŠΈ μœˆλ„μš° 관리 μ „λž΅, λΉŒλ“œ νŠΈλŸ¬λΈ”μŠˆνŒ… μ›Œν¬ν”Œλ‘œμš°λ₯Ό μ œκ³΅ν•©λ‹ˆλ‹€. μ„±λŠ₯ μ΅œμ ν™”λ₯Ό λͺ©ν‘œλ‘œ ν•˜λŠ” νŒ€κ³Ό μ—”μ§€λ‹ˆμ–΄λ₯Ό μœ„ν•œ 포괄적 κ°€μ΄λ“œμ΄λ©°, 싀무 μ μš©μ— ν•„μš”ν•œ ν”„λž˜κ·Έλ¨ΌνŠΈμ™€ μ›Œν¬ν”Œλ‘œμš°λ₯Ό ν¬ν•¨ν•©λ‹ˆλ‹€.

Core Features & Use Cases

  • λͺ¨λΈλ³„ μ„±λŠ₯ νŠΉμ„± 비ꡐ와 μ΅œμ ν™” μ „λž΅
  • μ»¨ν…μŠ€νŠΈ μœˆλ„μš° 관리 μ „λž΅μ˜ 섀계 및 적용
  • λΉŒλ“œ νŠΈλŸ¬λΈ”μŠˆνŒ… μ›Œν¬ν”Œλ‘œμš°μ˜ 단계별 ꡬ성 및 μ‹€ν–‰ μ˜ˆμ‹œ
  • Use Case: λŒ€κ·œλͺ¨ μ„œλΉ„μŠ€μ˜ λͺ¨λΈ 선택과 μ„€μ •μœΌλ‘œ 응닡 μ‹œκ°„κ³Ό λΉ„μš©μ˜ κ· ν˜•μ„ 맞좀

Quick Start

Haiku, Sonnet, λ˜λŠ” Opus 쀑 ν•˜λ‚˜μ˜ λͺ¨λΈμ— λŒ€ν•΄ μ„±λŠ₯ μ΅œμ ν™” μ›Œν¬ν”Œλ‘œμš°λ₯Ό μ‹œμž‘ν•˜λ €λ©΄ ν•΄λ‹Ή λͺ¨λΈμ˜ 레퍼런슀λ₯Ό ν™•μΈν•˜κ³  ꢌμž₯ 섀정을 μ μš©ν•˜λΌ.

Frequently Asked Questions about performance-reference

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

FAQPage Schema
How do I optimize model performance across Haiku, Sonnet, and Opus deployments?β–Ό

To optimize model performance, apply model-specific profiling and context-window strategies tailored to Haiku, Sonnet, and Opus. This reference provides structured tuning workflows to benchmark and adjust settings for each deployment.

What is the best way to manage context windows for large-scale model deployments?β–Ό

Managing context windows requires designing and applying specific strategies that balance response time and cost. This involves profiling model characteristics and implementing structured context management techniques during performance optimization.

How do I benchmark and tune Haiku versus Opus for performance optimization?β–Ό

Benchmarking Haiku versus Opus involves comparing model-specific performance characteristics and applying recommended tuning settings. The reference outlines distinct optimization strategies for each model tier to guide selection and configuration.

Can I use this performance optimization workflow for build troubleshooting?β–Ό

Yes, the performance optimization workflow includes a dedicated build troubleshooting process. It provides step-by-step configuration guidance and structured references to resolve issues during model deployment and tuning.

When do I need specific context window strategies for model performance tuning?β–Ό

You need context window strategies when balancing response time and cost in large-scale services. Designing these strategies ensures optimal model performance across different deployment tiers like Haiku, Sonnet, and Opus.