·cuda

CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel performance.

18Installs·0Trend·@technillogue

Installation

$npx skills add https://github.com/technillogue/ptx-isa-markdown --skill cuda

SKILL.md

Measure before guessing. GPU performance is deeply counterintuitive. Profile first, hypothesize second, change third, verify fourth.

Small, isolated changes. CUDA bugs compound. Make one change, test it, commit it. Resist the urge to "fix everything at once."

printf is your strongest tool. When debuggers fail, when tools produce inscrutable output, printf in device code reveals truth. Don't be embarrassed to use it extensively.

CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel performance. Source: technillogue/ptx-isa-markdown.

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Facts (cite-ready)

Stable fields and commands for AI/search citations.

Install command
npx skills add https://github.com/technillogue/ptx-isa-markdown --skill cuda
Category
{}Data Analysis
Verified
First Seen
2026-02-05
Updated
2026-02-18

Quick answers

What is cuda?

CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel performance. Source: technillogue/ptx-isa-markdown.

How do I install cuda?

Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/technillogue/ptx-isa-markdown --skill cuda Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor

Where is the source repository?

https://github.com/technillogue/ptx-isa-markdown

Details

Category
{}Data Analysis
Source
skills.sh
First Seen
2026-02-05

Related Skills

None