Conciseness

The body avoids explaining concepts Claude already knows, but it repeats the same AutoModelForCausalLM.from_pretrained + HqqConfig snippet nearly verbatim in 'Quick start', 'HuggingFace integration', 'Quantize and save', and 'Workflow 1', and demonstrates mixed precision twice (layer_configs dict and dynamic_config). It fits anchor 3 — mostly efficient but could be tightened by consolidating the duplicate snippets.

Actionability

Nearly all guidance is concrete, executable code covering install, basic quantization, HF, vLLM, PEFT, and troubleshooting, matching the good-example pattern. Not 5 because a few snippets are not copy-paste runnable as-is: 'input_tensor' is never defined in the basic example, Workflow 2 uses 'tokenizer' without loading it, and the Trainer example references undefined 'train_dataset'/'data_collator'.

Workflow Clarity

Both workflows are explicitly numbered, and Workflow 1 includes a real validation checkpoint ('3. Verify quality' with generation test code) before saving; 'Common issues' provides error-recovery fixes (OOM → sequential loading, poor 2-bit quality → smaller group size). Not 5 because the recovery guidance is not framed as validate→fix→retry loops inside the workflows, and Workflow 2 has a gap (tokenizer never loaded before benchmarking).

Progressive Disclosure

The References section clearly signals two real one-level-deep files (references/advanced-usage.md, references/troubleshooting.md), both of which exist and cover genuinely advanced material (sensitivity-based quantization, ONNX export, benchmarking, debugging). Not 5 because the main body still inlines substantial advanced content (full PEFT/Trainer setup, vLLM serving, per-backend tables) that overlaps what belongs in the reference files, making SKILL.md longer than an overview needs to be.

Specificity

The description names the domain ('Half-Quadratic Quantization for LLMs') and one concrete action ('quantizing models to 4/3/2-bit precision'), but coverage is not comprehensive — capabilities shown in the body such as backend selection, LoRA/PEFT fine-tuning, and saving/deploying quantized models are absent. It matches anchor 3 (domain plus 1-2 concrete actions); not 4 because several specific actions from the skill are missing, not 2 because the precision levels and calibration-free property are concrete.

Completeness

Both parts are explicit: what it does ('Half-Quadratic Quantization for LLMs without calibration data') and when to use it ('Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers'). This matches the anchor-5 example structure with a literal 'Use when...' clause and concrete trigger phrases.

Trigger Term Quality

Natural user phrases are present: 'quantizing models', '4/3/2-bit precision', 'calibration datasets', 'fast quantization', 'vLLM', 'HuggingFace Transformers'. Not 5 because common variations a user would say are missing — the skill's own name 'HQQ', 'model compression', 'reduce memory usage', 'shrink model' — and no file/format-style shorthand.

Distinctiveness Conflict Risk

The calibration-free angle ('without needing calibration datasets') is a genuinely distinctive trigger vs. GPTQ/AWQ skills, and precision levels plus vLLM/HF deployment are specific. Not 5 because generic quantization requests ('quantize this model to 4-bit') could plausibly match several quantization skills, since neither 'HQQ' nor an explicit differentiator from calibration-based methods appears as a trigger term.