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recommendation-engine

Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews 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.

DimensionReasoningScore

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

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly and concisely states what the skill does and when to use it, with concrete trigger conditions. The only weakness is modest synonym coverage in its trigger terms.

Suggestions

Add natural trigger synonyms such as 'recommender', 'similar items', or 'users also bought' to broaden keyword matching.

Consider mentioning ranking/relevance metrics terms users might say (e.g., 'top-N recommendations') in the trigger clause.

DimensionReasoningScore

Specificity

The description names concrete, specific capabilities — 'Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches' — listing multiple specific approaches, matching the comprehensive-coverage anchor. It is not a 4 because no meaningful capability area is missing; evaluation and cold start appear as explicit trigger conditions.

5 / 5

Completeness

Both parts are explicit: the 'what' ('Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches') and a concrete 'Use for...' clause with specific trigger conditions. This matches the level-5 anchor exactly; it is not a 4 because the 'when' clause is already explicit and specific.

5 / 5

Trigger Term Quality

'product recommendations', 'personalization', 'cold start', 'sparsity', 'quality evaluation' are phrases users would naturally say, giving good keyword coverage. It is not a 5 because common synonyms and variations ('recommender', 'suggested items', 'similar items', 'users also bought') are absent.

4 / 5

Distinctiveness Conflict Risk

A clear niche (recommendation systems) with distinct triggers ('product recommendations', 'personalization', 'cold start') minimizes conflict with other skills. Nothing in the description is generic enough to fire for unrelated work.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository

secondsky/claude-skills

Path

plugins/recommendation-engine/skills/recommendation-engine/SKILL.md

Reviewed

Table of Contents

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