building-recommendation-systems
Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. it analyzes user preferences, item features, and interaction data to generate personalized recommendations... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill building-recommendation-systems28
Quality
12%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/ai-ml/recommendation-engine/skills/building-recommendation-systems/SKILL.mdDiscovery
17%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description suffers from placeholder trigger text that provides no actual guidance for skill selection. While it identifies the recommendation systems domain and mentions technical approaches, the 'Use when' clause is completely non-functional boilerplate. The description also incorrectly uses imperative voice ('Execute this skill empowers') rather than third person.
Suggestions
Replace the placeholder trigger text with specific natural language triggers like 'Use when user asks for product recommendations, movie suggestions, personalized content, or mentions recommendation engines'
Add concrete user-facing keywords users would actually say: 'recommendations', 'suggest items', 'personalized suggestions', 'what should I watch/buy/read'
Rewrite opening to use proper third person voice: 'Constructs recommendation systems using collaborative filtering...' instead of 'Execute this skill empowers AI assistant to construct...'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (recommendation systems) and lists approaches (collaborative filtering, content-based filtering, hybrid), but the actions are somewhat vague ('analyzes', 'generate recommendations') rather than concrete specific operations. | 2 / 3 |
Completeness | While it partially describes 'what' (building recommendation systems), the 'when' clause is entirely placeholder text ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose') providing zero actual guidance. | 1 / 3 |
Trigger Term Quality | Uses technical jargon like 'collaborative filtering' and 'content-based filtering' that users wouldn't naturally say. The trigger guidance is completely generic placeholder text ('relevant phrases based on skill purpose') with no actual keywords. | 1 / 3 |
Distinctiveness Conflict Risk | The recommendation system domain is somewhat specific, but the generic trigger language and vague phrasing could cause conflicts with other data analysis or ML-related skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a template-style document that describes what a recommendation engine skill would do rather than providing actionable guidance. It lacks any executable code, concrete examples, or specific implementation details. The content is padded with generic boilerplate sections and explanations of concepts Claude already understands.
Suggestions
Replace abstract descriptions with executable Python code examples showing actual collaborative filtering and content-based implementations using specific libraries like surprise or implicit
Remove generic boilerplate sections (Prerequisites, Instructions, Output, Error Handling) that provide no skill-specific value
Add concrete workflow with validation steps: data loading -> preprocessing -> train/test split -> model training -> evaluation metrics -> iteration
Provide specific input/output examples with actual data schemas and expected recommendation output formats
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive padding explaining concepts Claude already knows (what recommendation systems are, types of filtering). Generic boilerplate sections like 'Prerequisites', 'Instructions', 'Output', and 'Error Handling' add no value and waste tokens. | 1 / 3 |
Actionability | No executable code anywhere despite claiming to 'generate code'. Examples describe what the skill 'will do' abstractly rather than providing actual implementation. No concrete commands, no copy-paste ready snippets, no specific library usage patterns. | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists vague phases without concrete steps. No validation checkpoints, no feedback loops for model evaluation, no clear sequence for building a recommendation system. Steps like 'Analyzing Requirements' are abstract descriptions, not actionable workflows. | 1 / 3 |
Progressive Disclosure | Content is organized into sections with headers, but it's a monolithic document with no references to external files. The structure exists but content that could be split (e.g., detailed algorithm implementations, evaluation metrics) is either missing or would bloat this file. | 2 / 3 |
Total | 5 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Reviewed
Table of Contents
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