triton-skill

Develop, debug, and optimize Triton and Gluon GPU kernels with tutorials and examples.

Updated Apr 14, 2026

Search Tags:#debugging #profiling #triton #autotune #gluon #kernel-development #gpu-kernels

Ready to run

npx skills add https://github.com/dotruru/claudemd --skill triton-skill-dotruru

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: triton-skill
Source: https://github.com/dotruru/claudemd/tree/main/skills/triton
Command: npx skills add https://github.com/dotruru/claudemd --skill triton-skill-dotruru

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers write, debug, and optimize Triton and Gluon GPU kernels by providing practical tutorials, source references, and hands-on examples in one guided package.

Core Features & Use Cases

Guided kernel development with Triton tutorials and Gluon samples.
Access to a curated set of kernel references and implementation patterns for matmul, reduction, and attention.
Use cases include converting CUDA kernels to Triton, autotuning, profiling, and performance analysis in research and production.

Quick Start

Start with 01-vector-add.py to initialize a basic Triton kernel.

Frequently Asked Questions about triton-skill

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

FAQPage Schema

How do I write and debug Triton GPU kernels for the first time?▼

Writing and debugging Triton GPU kernels starts with basic examples like vector addition. You can use guided tutorials to learn kernel development step by step, progressing to more complex patterns like matmul and reduction operations.

What is the best way to convert existing CUDA kernels to Triton?▼

Converting CUDA kernels to Triton involves translating CUDA-style logic into Triton's programming model. This Skill provides structured references and practical examples to help you translate and optimize your existing GPU kernel code.

How does autotuning work for optimizing Triton GPU kernels?▼

Autotuning in Triton optimizes GPU kernel performance by searching through configuration parameters. You can use provided tutorials to learn how to apply autotuning and profiling techniques for performance analysis in both research and production environments.

Can I use Triton for FP8 and FP4 quantization in GPU kernel development?▼

Yes, Triton supports FP8 and FP4 quantization in GPU kernel development. The Skill covers exploring these quantization methods alongside Tensor Memory Architectures to help you optimize and scale your kernel implementations.

Does this Skill provide examples for matmul, reduction, and attention kernels in Triton?▼

Yes, it provides a curated set of kernel references and implementation patterns specifically for matmul, reduction, and attention. These examples guide you through common GPU kernel development scenarios and optimization strategies.

Do I need access to the Triton repository to use these kernel tutorials?▼

Yes, you need access to the Triton repository content via the provided paths to use this Skill effectively. The tutorials and source references rely on accessing the repository to demonstrate kernel development, debugging, and profiling.