infra-skills

AI Infrastructure Agent Skills ⚠️ WARNING This project is under active development and heavily generated by LLMs without strict proofreading. Use with caution and verify all code before production use.

A collection of specialized agent skills for AI infrastructure engineers, covering both technical development (GPU kernels, distributed training, inference optimization) and soft skills (flowchart creation, presentation design). Overview

This repository provides expert-level skills tailored for AI infrastructure engineering. Each skill packages domain knowledge, code examples, and best practices to transform Claude into a specialized assistant for specific frameworks and workflows—from writing high-performance CUDA kernels to creating professional technical presentations. Construction Methodology (Unless Otherwise Specified)

1. Knowledge Gathering: Use Gemini DeepResearch to collect comprehensive, up-to-date information on target frameworks 2. Skill Development: Transform research into structured skills using skill-creator in Claude Code 3. Validation: Test skill-generated code examples to ensure correctness 4. Maintenance: Regular updates based on latest official documentation Available Skills TileLang Developer Write high-performance GPU kernels using TileLang for NVIDIA, AMD, and Ascend hardware.

Capabilities: Matrix multiplication (GEMM) kernels FlashAttention implementations DeepSeek MLA operators Performance optimization (swizzle layouts, pipelining, warp specialization) Cross-platform kernel development

Status: ✅ Complete Megatron Memory Estimator Estimate GPU memory usage for Megatron-based MoE and dense models. Built upon megatron_memory_estimator.

Capabilities: Estimate memory from HuggingFace configs Support for MoE models (DeepSeek-V3, Qwen, etc.) Parallelism strategy comparison (TP/PP/EP/CP) Memory optimization recommendations

Status: ✅ Complete SLIME User Guide for using SLIME (LLM post-training framework for RL Scaling). Built upon THUDM/slime.

Capabilities: RL training setup and configuration (GRPO, GSPO, PPO, Reinforce++) Multi-turn tool calling and agent workflows Custom reward models and generation functions Megatron and FSDP backend configuration SGLang integration and optimization Dynamic sampling and partial rollout Multi-node distributed training

Status: ✅ Complete

Prompt to create this skill, with Sonnet 4.5:

TikZ Flowchart Create professional flowcharts and architecture diagrams using LaTeX TikZ with standardized styles.

Capabilities: Professional flowcharts with Google Material-like color palette Standardized node types (data, memory, operation, kernel boxes) Architecture diagrams and process flows Grouping and layout best practices Clean orthogonal edges and relative positioning

Example Output: QAT Flowchart | Anthropic Theme