The description names the domain (research novelty verification) and one core action (verify research idea novelty against recent literature), but doesn't list multiple specific concrete actions like searching databases, comparing methodologies, generating novelty reports, etc.

Clearly answers both 'what' (verify research idea novelty against recent literature) and 'when' (explicit 'Use when' clause with specific trigger phrases and the scenario of wanting to verify novelty before implementing).

Excellent coverage of natural trigger terms including both Chinese ('查新', '有没有人做过') and English ('novelty check', 'check novelty') variations, plus the natural phrasing 'verify a research idea is novel before implementing'. These are terms users would naturally say.

Very distinct niche — research novelty checking is a specific task unlikely to conflict with general literature review, code review, or other research skills. The bilingual trigger terms and specific use case make it clearly distinguishable.

The skill is mostly efficient but includes some unnecessary elaboration. The 'Important Rules' section contains useful but somewhat verbose guidance, and some phrasing could be tightened (e.g., explaining what 'applying X to Y' means). The constants section and phase descriptions are reasonably lean.

The skill provides a clear structured workflow with specific phases and a concrete output template, but lacks executable code examples. The MCP call in Phase C shows only a config snippet and a vague prompt description rather than a copy-paste ready invocation. The search instructions in Phase B are directional rather than concrete (e.g., no actual query templates or specific tool invocation syntax).

The four phases (A-D) provide a clear sequence, but there are no validation checkpoints or feedback loops. If the literature search yields ambiguous results, there's no guidance on how to iterate. If the cross-model verification disagrees with the search findings, there's no error recovery step. For a research verification workflow where false positives/negatives have significant consequences, this is a notable gap.

The skill references external files (shared-references/review-tracing.md, tools/save_trace.sh) which is good progressive disclosure, but the main content is somewhat monolithic — the output template and important rules could potentially be separated. The references are one-level deep and clearly signaled, but the inline report template is lengthy and could be in a separate file.