building-neural-networks

# Neural Network Builder This skill provides automated assistance for neural network builder tasks. ## Overview This skill empowers Claude to design and implement neural networks tailored to specific tasks. It leverages the neural-network-builder plugin to automate the process of defining network architectures, configuring layers, and setting training parameters. This ensures efficient and accurate creation of neural network models. ## How It Works 1. **Analyzing Requirements**: Claude analyzes the user's request to understand the desired neural network architecture, task, and performance goals. 2. **Generating Configuration**: Based on the analysis, Claude generates the appropriate configuration for the neural-network-builder plugin, specifying the layers, activation functions, and other relevant parameters. 3. **Executing Build**: Claude executes the `build-nn` command, triggering the neural-network-builder plugin to construct the neural network based on the generated configuration. ## When to Use This Skill This skill activates when you need to: - Create a new neural network architecture for a specific machine learning task. - Modify an existing neural network's layers, parameters, or training process. - Design a neural network using specific layer types, such as convolutional, recurrent, or transformer layers. ## Examples ### Example 1: Image Classification User request: "Build a convolutional neural network for image classification with three convolutional layers and two fully connected layers." The skill will: 1. Analyze the request and determine the required CNN architecture. 2. Generate the configuration for the `build-nn` command, specifying the layer types, filter sizes, and activation functions. ### Example 2: Text Generation User request: "Define an RNN architecture for text generation with LSTM cells and an embedding layer." The skill will: 1. Analyze the request and determine the required RNN architecture. 2. Generate the configuration for the `build-nn` command, specifying the LSTM cell parameters, embedding dimension, and output layer. ## Best Practices - **Layer Selection**: Choose appropriate layer types (e.g., convolutional, recurrent, transformer) based on the task and data characteristics. - **Parameter Tuning**: Experiment with different parameter values (e.g., learning rate, batch size, number of layers) to optimize performance. - **Regularization**: Implement regularization techniques (e.g., dropout, L1/L2 regularization) to prevent overfitting. ## Integration This skill integrates with the core Claude Code environment by utilizing the `build-nn` command provided by the neural-network-builder plugin. It can be combined with other skills for data preprocessing, model evaluation, and deployment. ## 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