chore(skills): promote runbook skills to managed-skills; drop superseded SKILL.md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- Removed superseded skills: omp_harness_config, peon_ping_integration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.

agent/managed-skills/freetoken-build-dankclaw/SKILL.md

@@ -0,0 +1,55 @@
+---
+name: freetoken-build-dankclaw
+description: "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)
+
+```bash
+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
+
+```bash
+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
+
+```bash
+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`).