Probe the machine's ML hardware/software environment and tell the agent what it can and cannot run BEFORE attempting any ML setup. Produces a JSON manifest (env_report.json) plus a human-readable capability report with tiered ✅/⚠️/❌ verdicts and concrete install commands. Use at the START of any MLE automation, or whenever the user asks about: CUDA / driver / PyTorch version compatibility, why flash-attention / xformers / apex / bitsandbytes / deepspeed won't install or build, which torch wheel to pick, how to set up an isolated env (uv/conda/venv), package conflicts (transformers↔tokenizers, numpy 2.x), or how to split a model across GPUs (tensor/pipeline/data parallel, FSDP). Covers NVIDIA, Apple MPS, CPU, Google TPU, and Chinese accelerators (昇腾 Ascend, 寒武纪 Cambricon, 海光 Hygon DCU, 沐曦 MetaX). Triggers: "环境探测", "能跑什么", "装不上", "编译失败", "CUDA 版本", "torch 版本", "flash-attn", "环境清单", "环境隔离", "并行策略", "tensor parallel".
Probe the machine's ML hardware/software environment and tell the agent what it can and cannot run BEFORE attempting any ML setup. Produces a JSON manifest (env_report.json) plus a human-readable capability report with tiered ✅/⚠️/❌ verdicts and concrete install commands. Use at the START of any MLE automation, or whenever the user asks about: CUDA / driver / PyTorch version compatibility, why flash-attention / xformers / apex / bitsandbytes / deepspeed won't install or build, which torch wheel to pick, how to set up an isolated env (uv/conda/venv), package conflicts (transformers↔tokenizers, numpy 2.x), or how to split a model across GPUs (tensor/pipeline/data parallel, FSDP). Covers NVIDIA, Apple MPS, CPU, Google TPU, and Chinese accelerators (昇腾 Ascend, 寒武纪 Cambricon, 海光 Hygon DCU, 沐曦 MetaX). Triggers: "环境探测", "能跑什么", "装不上", "编译失败", "CUDA 版本", "torch 版本", "flash-attn", "环境清单", "环境隔离", "并行策略", "tensor parallel".
输出语种跟随用户的提问语种 (match the user's query language). 判断用户这次
请求用的是哪种语言,所有给人看的产物都用那种语言:你对用户说的话、ENV_CAPABILITY.md
的标题与正文、裁决表里的"理由/行动"。用户用英文问("help me inspect the env")就
全英文,用中文问就中文,其他语种同理。
