Adds agent/ref/ (ptx, cuda-cpp, triton, cute-cutlass) and agent/tools/check_toolchain.py.
Every instruction and API was compiled with the container's own nvcc on sm_90a / CUDA 12.8: 39/41 PTX instructions assemble (tcgen05 is Blackwell-only and recorded as such), 24/24 CUDA C++ constructs compile, and the Triton tables come from introspecting the installed 3.6.0.
Measured facts that are easy to get wrong: wgmma.m64nNk16.f32 needs exactly N/2 accumulator registers per thread, so an m64n256k16 tile spends 128 registers on the accumulator alone; and Triton 3.6 accepts cache_modifier='.cg' with eviction_policy='evict_first' but ptxas rejects the pair.
check_toolchain.py re-derives the tables on whatever GPU the agent actually has.
Add agent/: the kernel-optimization skill and four subagents
A Claude Code skill (7-step loop: harness -> roofline -> algorithm -> toolchain -> implement -> profile -> specialize -> iterate) plus kernel-algorithmist, kernel-implementer, kernel-profiler and kernel-verifier.
Tools are verified on device: roofline.py measures the machine instead of quoting a datasheet, profile.sh runs nsys-then-ncu and emits a diagnosis rather than a metric dump, dump_ir.sh extracts Triton/Inductor IR and SASS.
README.md has the install steps and the two things that bite: ncu needs SYS_ADMIN or it exits ERR_NVGPUCTRPERM, and counters are hints not verdicts -- cuBLAS's own GEMM profiles at 15% occupancy with 168 regs/thread while being essentially optimal.
