Content
78%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, code-centric skill body with excellent progressive disclosure into verified reference files and a clear quick-start workflow. The main gaps are small correctness issues in the inline examples (undefined names, non-existent module imports) and some duplicated code snippets.
Suggestions
Fix CollaborativeFilter.fit to store user_item_matrix (e.g., self.user_item_matrix = user_item_matrix) so recommend_for_user actually runs, and define or reference ContentBasedFilter for HybridRecommender.
Replace the Quick Start's imports of non-existent modules (recommendation_engine, evaluation_metrics, cold_start) with pointers to the actual reference files or include the minimal implementations inline.
De-duplicate the TruncatedSVD example and the already-interacted filtering pattern, which each appear twice in the body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-first and efficient, avoiding explanations of concepts Claude already knows. It is not a 5 because of duplicated material: the TruncatedSVD example appears both in its own section and in the sparsity issue, and the already-interacted filtering pattern is repeated between the CF class and issue #4. | 4 / 5 |
Actionability | Mostly concrete, executable code with a step-by-step Quick Start. It is not a 5 because several snippets have gaps: CollaborativeFilter.fit never stores user_item_matrix yet recommend_for_user uses it, HybridRecommender references an undefined ContentBasedFilter, and the Quick Start imports recommendation_engine/evaluation_metrics/cold_start modules that do not exist in the bundle. | 4 / 5 |
Workflow Clarity | The 5-step Quick Start gives a clear, numbered sequence (prepare matrix, train, recommend, evaluate, handle cold start) with evaluation acting as a checkpoint. It is not a 5 because there are no explicit validation thresholds or feedback loops (e.g., what to do when precision@k is low). | 4 / 5 |
Progressive Disclosure | The 'When to Load References' section clearly signals all four one-level-deep reference files with accurate descriptions of their contents, and all four files exist in the bundle and match those descriptions. The body keeps only overview-level implementations, splitting detail appropriately. | 5 / 5 |
Total | 17 / 20 Passed |