Conciseness

The gotchas are genuinely non-obvious domain knowledge (BOS token ID 2 corrupting attention states, embedder lookup-table quantization risk, KV scale alignment) rather than concepts Claude already knows, so the body is largely efficient. Minor trimmable padding remains — the opening paragraph restates the description and the horizontal rules add bulk — keeping it below anchor 5.

Actionability

Four complete command blocks with full flag lists and one concrete value ('--stop_token=106' for Gemma 3) are mostly executable, but the placeholder arguments ('{model_path}', '{true_or_false}') and the malformed E2E script invocation ('bash cd litert_torch/... ./run_quantize_and_inference.sh') leave minor gaps that keep it from copy-paste-ready anchor 5.

Workflow Clarity

The calibrate → merge → quantize → inference-test sequence is clearly ordered across the numbered command sections, but there are no validation or verification checkpoints anywhere (e.g. confirming the merged calibration JSON exists before quantizing, or sanity-checking quantized output before testing). Because this is a batch workflow over multi-task calibration directories and model suites, the rubric caps workflow clarity at 3.

Progressive Disclosure

No bundle files exist, and the single-file body is well-organized with clear section headers (triggers, gotchas, command patterns, driver tool) that make navigation easy. It falls short of anchor 5 only because the ~170-line gotcha reference material sits fully inline in SKILL.md when it could be split into a reference file.

Specificity

Lists multiple concrete actions — 'calibrate, merge, and statically quantize litert LLM models', 'align KV cache parameters across models', 'protect sensitive layers in Float32', 'run quantized inference testing' — giving comprehensive coverage of the skill's domain. It exceeds anchor 4 because the action list spans the entire workflow rather than having minor gaps.

Completeness

Explicitly answers both questions: what ('Assists the user to calibrate, merge, and statically quantize litert LLM models') and when ('Use when the user wants to run LLM calibration, merge task JSON results, align KV cache parameters...') with concrete trigger phrases and an explicit 'Don't use for' exclusion.

Trigger Term Quality

Natural phrases like 'LLM calibration', 'merge task JSON results', 'KV cache parameters', and 'quantized inference testing' would be said by users needing this skill, plus a named model ('Gemma 3'). A few natural terms are missing (e.g. 'int8', 'A8W8/A16W8', 'TFLite'), keeping it below anchor 5.

Distinctiveness Conflict Risk

A clear niche ('litert LLM models (such as Gemma 3)') with distinct triggers and an explicit negative boundary ('Don't use for JAX/PyTorch custom quantization configurations or non-litert models'), minimizing overlap with other quantization skills.