Skill Claude CodeCodex
High-level guide for integrating a new DiT model into cache-dit: Cache (BlockAdapter/ForwardPattern), Context Parallelism, Tensor Parallelism, Text Encoder Parallelism (TE-P), VAE Parallelism (VAE-P), generate CLI, installation, testing workflow, and detailed references. Use when adding support for a new diffusion…
Skill Claude CodeCodex
Use when writing, debugging, porting, reviewing, or optimizing CUDA C++ or PTX kernels; investigating CUDA Runtime or Driver API behavior; profiling kernels with Nsight Systems or Nsight Compute; or reasoning about Tensor Core instructions, shared memory, bank conflicts, occupancy, async copy, TMA, WGMMA, and…
Skill Claude CodeCodex
Use when writing, modifying, porting, or optimizing CuTe DSL GPU kernels in Python; reading CuTe DSL API reference material; integrating a CuTe DSL kernel into a project; or rewriting an existing CUDA or C++ operator into CuTe DSL while preserving correctness and performance expectations.
Skill Claude CodeCodex
Use when writing, debugging, porting, reviewing, or optimizing CUTLASS or CuTe C++ kernels and templates; navigating CUTLASS examples, collectives, epilogues, pipelines, GEMM schedules, or CuTe headers; or analyzing template configuration, tiling, memory movement, and kernel structure for Hopper or Blackwell GPUs.
Skill Claude CodeCodex
Use when doing operator migration or kernel migration for CUDA, Triton, or custom ops in cache-dit; porting kernels from nunchaku, deepcompressor, or other repos; designing operator registration and public wrappers; wiring build and packaging for optional extensions; or reviewing an operator migration plan. Guides…
Skill Claude CodeCodex
Use when integrating a new PTQ workflow into cache-dit; designing quantize/load API shape, backend-specific config validation, save/load manifests, benchmark and regression tests, or reviewing a PTQ integration plan. Uses the SVDQ PTQ integration only as a style and coverage reference. Do not copy the SVDQ…
Skill Claude CodeCodex
Write optimized Triton GPU kernels for deep learning operations. Covers the full spectrum from basic vector ops to Flash Attention, persistent matmul, fused normalization, quantized GEMM, and memory-efficient patterns.