chore(memory): snapshot auto-learned memory and renamed workstation skills

--home-blank-.omp--: MEMORY.md +140 / learned.md / memory_summary.md /
raw_memories.md +1002 rewritten by auto-learn after the model rename,
frank-medium split-balance and bottleneck studies. Skills renamed to
single-topic buckets: dotfile-git-workflow -> ghostty-text-aa +
niri-display-rotation + noctalia-theme, handy-service-mgmt ->
handy-flm-inference, inference-runtime -> llama-cpp-parallelism.

--home-blank-Projects-serverAppConfig--: 38 new rollout summaries,
raw_memories.md/MEMORY.md refreshed, skills re-bucketed (bench-protocol,
llm-fleet-model-swap, rootless-quadlet-log-retrieval, orchestration-quadlet).

Per-worktree bucket (wt-20261006-122204) is now gitignored, not committed.

agent/memories/--home-blank-Projects-serverAppConfig--/memory_summary.md

@@ -1 +1 @@
-Fleet consists of dankserver (2x R9700) and dankclaw (Strix Halo + 3090). LLM units: frank-medium (Qwen3.8-27B, layer-split 35:30, 16GB cache), frank-large (Halogen, 14.4GB KV pool), and frank-small (Gemma4-12B vLLM). Deployment is Quadlet-based with a path-aware planning pipeline. Key technical constraints include RCCL atomic routing, Halogen memory pinning, and the OMP session persistence model.
+Current fleet is stable but marginal on `dankserver` RAM (4600 MT/s). `frank-large` (Halogen) and `frank-primary` (VLLM) are the main inference providers. Watch for `max_tokens` 400 errors on large prompts and orphaned `llama-server` processes after benching. `frank-medium` requires 16384 MiB `CACHE_RAM` for hybrid DeltaNet states. Quadlet units ensure boot persistence. Use `ak shell` for Authentik user management. Distill skills to vaults to reduce context bloat (~38k tokens overhead).