NVIDIA/TensorRT-LLM/ad-accuracy-debug A Agent Skill Active
Debug AutoDeploy accuracy regressions vs a reference score (PyTorch backend or published baseline).
Browse SKILL.md-based agent skills for Claude Code, Codex, Cursor, Gemini CLI and more.
Debug AutoDeploy accuracy regressions vs a reference score (PyTorch backend or published baseline).
Claude Code skill (trtllm-agent-toolkit): implement or extend TensorRT-LLM AutoDeploy fusion transforms under transform/library/ in a TensorRT-LLM checkout.
Check whether AutoDeploy YAML configs were actually applied by analyzing server logs and optionally graph dumps (AD_DUMP_GRAPHS_DIR).
Enable and interpret TensorRT-LLM AutoDeploy FX graph text dumps via AD_DUMP_GRAPHS_DIR.
Visualize a specific transformer decoder layer from an AutoDeploy FX graph text dump as a hierarchical DOT/PNG diagram.
Translates a HuggingFace model into a prefill-only AutoDeploy custom model using reference custom ops, validates with hierarchical equivalence tests.
Compile TensorRT-LLM on a compute node inside a Docker container.
Compile TensorRT-LLM on a SLURM cluster.
Write and implement GPU kernels using NVIDIA CuTe DSL (CUTLASS 4.x Python API) — NOT for Triton, CUDA C++, or conceptual explanations.
Optimize existing Triton kernels for NVIDIA TileIR backend on Blackwell GPUs (sm_100+).
ONLY for OpenAI Triton (@triton.jit) kernel development.
Performance analysis coordination workflow.
Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces.
Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned).
Analyze ncu (NVIDIA Nsight Compute) profiling output: SOL% bottleneck classification, roofline analysis, occupancy diagnosis, memory hierarchy analysis, warp stall analysis, metr...
Nsight Systems (nsys) CLI for system-level timeline profiling.
Performance optimization coordination playbook.
Apply CUDA Graphs to PyTorch workloads — API selection (torch.compile, PyTorch make_graphed_callables, TE make_graphed_callables, MCore CudaGraphManager, FullCudaGraphWrapper, m...
Identify and eliminate host-device synchronizations in PyTorch code.
Code instrumentation for timing workloads.
Best practices for contributing code to TensorRT-LLM.
Systematic approach to exploring the TensorRT-LLM codebase before implementing new features or optimizations.
Upgrade flashinfer-python version in TensorRT-LLM.
Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability.
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs.