Conciseness

The style is efficient (commands and tables, no teaching of concepts Claude already knows), but there is real duplication that could be tightened: the Metal/CUDA/ROCm build commands appear in both 'Installation' and again in each 'Hardware acceleration' subsection; the 'Choosing quantization' block restates the quantization table above it; and both benchmark tables largely duplicate tables already in references/optimization.md (M3 Max 50 tok/s, 7950X 35, i9-13900K 30, RTX 4090 120 tok/s appear in both). This is more than the 'minor instances that could be trimmed' of anchor 4 but the content is still mostly efficient, so anchor 3 is the best fit.

Actionability

Nearly everything is copy-paste-ready executable shell: brew install/git clone + make with per-backend flags, huggingface-cli download, llama-cli and llama-server invocations with real flags, and a working curl request to the OpenAI-compatible endpoint. Specific examples cover the common cases (simple chat, interactive chat, server mode) with concrete values (-ngl 32, -c 4096, --batch-size 512), matching the 'fully executable; specific examples cover the common cases' anchor. The 'Choosing quantization' block is comments-only, but it is guidance on format selection, not a command that was supposed to run.

Workflow Clarity

The Quick start is coherently sequenced (Installation -> Download model -> Run inference -> Server mode), but there are no validation or verification checkpoints anywhere — e.g., no step confirming the model file downloaded fully, that the server is up before the curl request, or that batch output is correct. The 'Batch processing' pattern (cat prompts.txt | ...) runs unvalidated over many prompts, and the rubric explicitly caps workflows involving batch operations without validation at 3, which takes precedence over the otherwise clear sequencing.

Progressive Disclosure

Good structure: a References section points to three real, one-level-deep files (references/quantization.md, references/server.md, references/optimization.md) with clear labels of what each contains, and each exists in the bundle. However, the split is imperfect: the SKILL.md body carries two full benchmark tables and hardware-tuning detail that duplicates references/optimization.md instead of deferring to it, and the ~250-line body is at the upper edge of an overview. This is the 'good structure; most content appropriately placed; minor organization gaps' anchor rather than the clean anchor-5 split.

Specificity

Concrete, quantified actions: "Runs LLM inference on CPU, Apple Silicon, and consumer GPUs" and "Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10x speedup vs PyTorch on CPU". It lists several specific capabilities in third-person voice, but coverage has minor gaps (no mention of the OpenAI-compatible server mode or grammar-constrained generation that the skill actually supports), so it matches the 'several specific actions; minor gaps' anchor rather than the fully comprehensive anchor 5. It is clearly above anchor 3, which requires only 1-2 concrete actions.

Completeness

Explicitly answers both questions with concrete triggers: the 'what' is stated ("Runs LLM inference on CPU, Apple Silicon, and consumer GPUs... Supports GGUF quantization (1.5-8 bit)") and the 'when' is an explicit use clause ("Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable"). This matches the anchor-5 good example's structure of capabilities followed by explicit trigger guidance; anchor 4 would require a weaker or less specific 'when'.

Trigger Term Quality

Strong natural trigger phrases: "CPU", "Apple Silicon", "M1/M2/M3 Macs", "AMD/Intel GPUs", "CUDA is unavailable", "edge deployment". However, common variations a user would actually say are missing — notably "llama.cpp" itself, "GGUF file" / ".gguf", and "local/offline inference" — so it falls just short of the 'comprehensive coverage including synonyms and file extensions' anchor 5, while being clearly above the 'good keyword coverage' reading of anchor 4's floor.

Distinctiveness Conflict Risk

The niche is sharply bounded by hardware boundary language — "without NVIDIA hardware", "when CUDA is unavailable" — which distinguishes it from competing inference skills (TensorRT-LLM, vLLM) that trigger on NVIDIA/CUDA contexts. Triggers name specific hardware (M1/M2/M3, AMD/Intel GPUs, Raspberry Pi-adjacent 'edge deployment'), giving minimal overlap risk, which matches the 'clear niche with distinct triggers' anchor.