Audit HLSL pixel shaders for GPU performance inefficiencies and optimizations.
npx skills add https://github.com/cwilliams5/Alt-Tabby --skill review-shaders
Please help me install this Agent Skill. Skill: review-shaders Source: https://github.com/cwilliams5/Alt-Tabby/tree/main/.claude/skills/review-shaders Command: npx skills add https://github.com/cwilliams5/Alt-Tabby --skill review-shaders
This Skill identifies and rectifies performance bottlenecks within HLSL pixel shaders, ensuring smooth, high-frame-rate rendering by optimizing GPU instruction usage.
Audit all HLSL shaders in src/shaders/ for performance waste and suggest optimizations.
High-intent search queries and answers about installing and using this skill.
You can optimize HLSL pixel shaders by auditing them for redundant transcendentals, suboptimal loops, and costly algebraic operations. Refactoring these GPU instruction patterns reduces ALU bottlenecks and improves overall rendering frame rates.
Low frame rates in HLSL shaders are often caused by GPU performance inefficiencies such as costly algebraic operations, suboptimal loops, and redundant transcendentals. Auditing pixel shaders helps identify and rectify these ALU and texture bottlenecks.
You can audit a shader library for ALU reduction and texture efficiency by analyzing HLSL pixel shaders against original GLSL sources. This process identifies math optimizations and redundant operations while ensuring visual equivalence is maintained after refactoring.
Yes, you can optimize DXBC shaders while ensuring visual equivalence with original GLSL sources. The audit compares optimized HLSL pixel shaders against the original GLSL sources to verify that visual output remains identical after algebraic simplifications.
The shader optimization process analyzes HLSL files located in the `src/shaders/` directory and its subdirectories. It scans this specific path structure to identify performance waste and suggest actionable GPU instruction refactoring.
cwilliams5Audit HLSL pixel shaders for GPU performance optimizations with visual equivalence proof.
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