triton-skill

Develop, debug, and autotune Triton and Gluon GPU kernels for ML workloads.

1|Updated Apr 4, 2026

Search Tags:#triton #autotune #gluon #gpu-kernel #kernel-development #tl.load

Ready to run

npx skills add https://github.com/m0at/claudemd --skill triton-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: triton-skill
Source: https://github.com/m0at/claudemd/tree/main/skills/triton
Command: npx skills add https://github.com/m0at/claudemd --skill triton-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines the development, debugging, and optimization of Triton and Gluon GPU kernels by consolidating local source access, tutorials, and kernel references for quick learning and productive work.

Core Features & Use Cases

Guided kernel development: from simple vector adds to complex fused attention with autotuning and device-specific optimizations.
Educational references: organized tutorials and examples covering tl.load, tl.store, tl.dot, triton.jit, gluon.jit, and MLIR concepts.
Practical workflow: supports porting CUDA kernels to Triton, profiling performance, and validating kernel correctness for ML workloads.

Quick Start

Run a quick tutorial to implement and test a vector-add 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 ML workloads?▼

To develop Triton GPU kernels, you use operations like tl.load, tl.store, and tl.dot within triton.jit to build and optimize custom ML workloads. You can debug and validate kernel correctness using provided tutorials and production references.

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

Porting CUDA kernels to Triton involves translating CUDA logic into Triton's Python-based DSL using block-level operations like tl.dot and tl.store. The Skill provides practical workflows and references to guide the porting and performance profiling process.

How does autotuning work for Triton GPU kernels?▼

Autotuning in Triton GPU kernels optimizes performance by searching over configurable kernel parameters like block sizes and instruction counts. It is supported directly in the development workflow to achieve device-specific optimizations for complex operations like fused attention.

Do I need local Triton source code to use Gluon and MLIR concepts?▼

Yes, accessing local Triton source code, tutorials, and kernel references is required. This local environment supports learning Gluon operations, gluon.jit, and MLIR concepts for effective kernel-level optimization and development.

Can I use this to optimize complex fused attention kernels?▼

Yes, you can optimize complex fused attention kernels using Triton and Gluon. The Skill supports guided kernel development from simple vector additions to advanced fused attention with autotuning and device-specific performance optimizations.

Why use Triton over other GPU kernel development approaches?▼

Triton streamlines GPU kernel development by consolidating local source access, tutorials, and references into a productive workflow. It allows you to write, autotune, and port CUDA kernels using higher-level abstractions like tl.dot and triton.jit.