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Frank 9bfa0be85d chore(skills): promote runbook skills to managed-skills; drop superseded SKILL.md

- 31 new managed skills (anubis/arr/authentik/hermes/freetoken/podman/llm-swap
  runbooks etc.) now tracked.
- Removed superseded skills: omp_harness_config, peon_ping_integration
  (replaced by user-authored peon-ping-* managed skills),
  rocmfp4-strix-benchmark-orchestration.
- New memory-root skills: niri_compositor_ops, omp_git_workflow,
  omp_model_config, quadlet-media-migration, homelab-service-triage,
  hermes-dankclaw-ops, frank-default-large-ops, caddy-auth-dns-playbook.

2026-09-09 16:20:28 -07:00

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name, description

name	description
freetoken-build-dankclaw	Build FreeToken on Dankclaw (Ubuntu 24.04) with CUDA — installs nvidia-cuda-toolkit, sets CUDA_HOME=/usr, overrides nvcc/CUDA version mismatch

FreeToken Build on Dankclaw

Dankclaw is Ubuntu 24.04 with ROCm already installed. FreeToken builds for CUDA (not ROCm) because the kernel package (_pinned_tensor) links against cudart.

Prerequisites (install if missing)

export PATH="/home/dank/.local/bin:$PATH"
export CUDA_HOME=/usr
export FREETOKEN_ALLOW_CUDA_MISMATCH=1

# CUDA toolkit (if not present):
sudo apt-get install -y nvidia-cuda-toolkit
# UV (if not present):
curl -LsSf https://astral.sh/uv/install.sh | sh

Key environment variables

Variable	Value	Why
`CUDA_HOME`	`/usr`	Ubuntu CUDA toolkit package places nvcc at `/usr/bin/nvcc` and libcudart at `/usr/lib/x86_64-linux-gnu/`
`FREETOKEN_ALLOW_CUDA_MISMATCH=1`	Set	nvcc is 12.4 but torch 2.11.0+cu130 ships CUDA 13.0 — safe to override
`PATH`	`/home/dank/.local/bin:$PATH`	uv installs here

Build procedure

cd /home/dank
git clone https://github.com/FlashML-org/FreeToken.git
cd FreeToken
uv venv && source .venv/bin/activate
uv pip install -e ".[accel]"

Verification

ft --version                    # → freetoken version 0.1.2
python3 -c "import torch; print(torch.__version__)"  # → 2.11.0+cu130
python3 -c "from freetoken.kernel import _pinned_tensor; print(_pinned_tensor.__file__)"
python3 -c "import flashinfer; print(flashinfer.__version__)"  # → 0.6.18
python3 -c "import sgl_kernel; print(sgl_kernel.__version__)"  # → 0.4.5

Notes

torch.cuda.is_available() may return False with "Can't initialize NVML" warning — this is the management library, not CUDA runtime. The kernels are built and linked correctly.
The NVIDIA driver modules may or may not be loaded; they don't affect the build.
ROCm is present on Dankclaw but FreeToken's kernel build specifically requires CUDA (links cudart).

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