--- name: building-recommendation-systems-a1135ca0204c description: | 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. allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 1.0.0 author: Jeremy Longshore license: MIT --- # Recommendation Engine This skill provides automated assistance for recommendation engine tasks. ## Overview This skill enables Claude to design and implement recommendation systems tailored to specific datasets and use cases. It automates the process of selecting appropriate algorithms, preprocessing data, training models, and evaluating performance, ultimately providing users with a functional recommendation engine. ## How It Works 1. **Analyzing Requirements**: Claude identifies the type of recommendation needed (collaborative, content-based, hybrid), data availability, and performance goals. 2. **Generating Code**: Claude generates Python code using relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch) to build the recommendation model. This includes data loading, preprocessing, model training, and evaluation. 3. **Implementing Best Practices**: The code incorporates best practices for recommendation system development, such as handling cold starts, addressing scalability, and mitigating bias. ## When to Use This Skill This skill activates when you need to: - Build a personalized movie recommendation system. - Create a product recommendation engine for an e-commerce platform. - Implement a content recommendation system for a news website. ## Examples ### Example 1: Personalized Movie Recommendations User request: "Build a movie recommendation system using collaborative filtering." The skill will: 1. Generate code to load and preprocess movie rating data. 2. Implement a collaborative filtering algorithm (e.g., matrix factorization) to predict user preferences. ### Example 2: E-commerce Product Recommendations User request: "Create a product recommendation engine for an online store, using content-based filtering." The skill will: 1. Generate code to extract features from product descriptions and user purchase history. 2. Implement a content-based filtering algorithm to recommend similar products. ## Best Practices - **Data Preprocessing**: Ensure data is properly cleaned and formatted before training the recommendation model. - **Model Evaluation**: Use appropriate metrics (e.g., precision, recall, NDCG) to evaluate the performance of the recommendation system. - **Scalability**: Design the recommendation system to handle large datasets and user bases efficiently. ## Integration This skill can be integrated with other Claude Code plugins to access data sources, deploy models, and monitor performance. For example, it can use data analysis plugins to extract features from raw data and deployment plugins to deploy the recommendation system to a production environment. ## Prerequisites - Appropriate file access permissions - Required dependencies installed ## Instructions 1. Invoke this skill when the trigger conditions are met 2. Provide necessary context and parameters 3. Review the generated output 4. Apply modifications as needed ## Output The skill produces structured output relevant to the task. ## Error Handling - Invalid input: Prompts for correction - Missing dependencies: Lists required components - Permission errors: Suggests remediation steps ## Resources - Project documentation - Related skills and commands --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/ai-ml/recommendation-engine/skills/building-recommendation-systems/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Claude Code Plugins Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/ai-ml/recommendation-engine/skills/building-recommendation-systems/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management Roles: Data Scientist, Growth Marketer, Marketing Analyst, ML / AI Engineer, Product Manager Specialties: API Tools: Pytorch, Scikit Learn, Tensorflow Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: bc4d3cd58f04fb6ca465da1b70593d8d7cb6e5ba3cdef1afb4ebe7f1f1067ff0