Content
85%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured procedural skill body with a clear, validated optimization workflow and excellent progressive disclosure to per-library references. Minor conciseness gains are available by trimming the trigger bullet list that duplicates the description.
Suggestions
Trim the 24-bullet 'When This Skill Applies' list to the distinct triggers not already covered by the frontmatter description to recover tokens.
Consider one or two more inline executable snippets (e.g. a CuPy drop-in or a transfer-once pattern) so the body is self-sufficient for the most common case without opening a reference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense, procedural, and assumes Claude's intelligence (e.g. the library-selection table, benchmark snippet); the main trim opportunity is the 24-bullet 'When This Skill Applies' list, which substantially overlaps the description's trigger terms. | 4 / 5 |
Actionability | Provides an executable benchmark snippet, named profiling tools (nsys, ncu, %gpu_timeit), and a concrete workload-to-library mapping table; the bulk of per-library code is correctly deferred to references rather than inlined. | 4 / 5 |
Workflow Clarity | A clearly sequenced 7-step workflow with an explicit validation step ('Validate semantics before speed') and a keep/revise/reject feedback loop in step 7, matching the explicit-validation-and-feedback-loop anchor. | 5 / 5 |
Progressive Disclosure | The body is an overview that signals one-level-deep references via a 'Reference Files' table; all 14 referenced files exist on disk, and inline detail is appropriately limited. | 5 / 5 |
Total | 18 / 20 Passed |