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.
The body is an exemplarily lean router skill: it makes the language-to-library decision unambiguous and points to authoritative upstream resources without duplicating them. Its only notable weakness is actionability — a minimal code example or named key annotators per library would let Claude act without first fetching external documentation.
Suggestions
Add a minimal copy-paste example per library (e.g. supervision-js BoxAnnotator and Python sv.BoxAnnotator annotating a frame) so Claude can act immediately.
Name the main annotator classes/functions for common cases (boxes, masks, labels, traces) with links to their doc sections rather than only top-level documentation URLs.
Briefly state how to detect an installed library usage skill (e.g. check the available-skills listing for the library name) to remove residual ambiguity in the upstream-guidance step.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every line is either a routing decision or a concrete pointer; there is no padding and no explanation of concepts Claude already knows. Lean and efficient throughout. | 5 / 5 |
Actionability | Concrete guidance exists (exact library names, real documentation URLs, an explicit language-to-library rule), but the body contains no code, commands, or named APIs — key details are delegated entirely to external docs. | 3 / 5 |
Workflow Clarity | A simple single-purpose skill under 50 lines whose single action — pick the library matching the environment, then follow upstream skill or official docs — is unambiguous, with the conditional branch resolved explicitly. | 5 / 5 |
Progressive Disclosure | Under 50 lines with no bundle files to split; two well-organized sections with clearly signaled one-level-deep external links, matching the simple-skill guideline for a top score. | 5 / 5 |
Total | 18 / 20 Passed |