The body is mostly efficient with concrete tool tables and executable code, but the lengthy "Prompt Engineering" section explains concepts and philosophy Claude largely already knows (e.g. why image models reason, extensive adjective-substitution tables), and could be tightened. Not the score-1 verbose concept primer, but above the lean anchor.

Provides fully executable tool.call / run_sdk_snippet code, concrete parameter tables, copy-paste retry and multi-view patterns, and an unambiguous tool-selection decision tree.

Multi-step processes are clearly sequenced with validation checkpoints and feedback loops: the design-marker 4-step process, the failed-task retry loop via element_id, and the mandatory visual_understanding step before image-to-image generation.

Content is well sectioned with one-level-deep, clearly signaled references (reference/image/image-search.md, reference/video/generation.md, etc.), but no references/ bundle directory actually exists, so the referenced paths are broken and navigation cannot be completed.

Names five concrete action areas ("project management, multimedia principles, AI image generation, web image search, and design marker processing"), which would anchor at 3, but the description uses second person voice ("you MUST load this skill first"), which the rubric penalizes by reducing specificity by 1.

Clearly answers what it does (the five design domains) and when to use it via explicit triggers ("Load for any canvas design task" and "When user message contains ... you MUST load this skill first").

Covers natural trigger phrasing users would say — "canvas design task" and "generate video/animation/clip on a canvas project" — alongside the explicit [@design_canvas_project:...] / [@design_marker:...] mention triggers.

Clear canvas-design niche anchored by the unique [@design_canvas_project:...] and [@design_marker:...] mention syntax, making false triggering by other skills unlikely.