Names a specific action ('remove unnecessary LLM-generated comments') and a domain ('code'), but only describes a single action rather than listing multiple concrete capabilities.

Describes what the skill does but has no 'Use when...' clause or equivalent explicit trigger guidance. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'what' is also minimal, this scores a 1.

Includes some relevant terms like 'comments', 'code', and 'LLM-generated', but misses common variations users might say such as 'clean up code', 'AI-generated comments', 'remove boilerplate comments', 'strip comments', or 'code cleanup'.

The phrase 'LLM-generated comments' is fairly distinctive and narrows the scope, but 'remove comments from code' could overlap with general code formatting or linting skills. The niche is somewhat clear but not sharply delineated.

Every section earns its place. The skill teaches pattern recognition through concrete examples rather than explaining what comments are or why clean code matters. No wasted tokens.

Provides concrete, real-world code examples across four languages showing exactly what to remove and what to keep. The before/after example is copy-paste demonstrable and leaves no ambiguity about the expected transformation.

This is a single-task skill (identify and remove bad comments) with a clear decision framework: remove narration/changelog/section markers/filler, keep why-comments/links/warnings/licenses/doc-comments. The workflow is unambiguous for this non-destructive, non-batch operation.

For a focused, single-purpose skill under 50 lines of instructional content, the structure is well-organized with clear sections (what to remove, what to keep, full example). No need for external references.