Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "neural-network-builder",
+  "description": "Build and configure neural network architectures",
+  "version": "1.0.0",
+  "author": {
+    "name": "Claude Code Plugins",
+    "email": "[email protected]"
+  },
+  "skills": [
+    "./skills"
+  ],
+  "commands": [
+    "./commands"
+  ]
+}

README.md

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+# neural-network-builder
+
+Build and configure neural network architectures

commands/build-nn.md

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+---
+description: Execute AI/ML task with intelligent automation
+---
+
+# AI/ML Task Executor
+
+You are an AI/ML specialist. When this command is invoked:
+
+1. Analyze the current context and requirements
+2. Generate appropriate code for the ML task
+3. Include data validation and error handling
+4. Provide performance metrics and insights
+5. Save artifacts and generate documentation
+
+Support modern ML frameworks and best practices.

plugin.lock.json

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+  "pluginId": "gh:jeremylongshore/claude-code-plugins-plus:plugins/ai-ml/neural-network-builder",
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+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
+  },
+  "manifest": {
+    "name": "neural-network-builder",
+    "description": "Build and configure neural network architectures",
+    "version": "1.0.0"
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+    "flags": []
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+}

skills/neural-network-builder/SKILL.md

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+---
+name: building-neural-networks
+description: |
+  This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## 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.

skills/neural-network-builder/assets/README.md

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+# Assets
+
+Bundled resources for neural-network-builder skill
+
+- [ ] example_configurations/: Directory containing example neural network configurations for different tasks.
+- [ ] visualization_templates/: Directory containing templates for visualizing neural network architectures and performance metrics.
+- [ ] sample_datasets/: Directory containing sample datasets for training and evaluating neural networks.

skills/neural-network-builder/references/README.md

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+# References
+
+Bundled resources for neural-network-builder skill
+
+- [ ] neural_network_fundamentals.md: A comprehensive guide to neural network fundamentals, including terminology, architectures, and training algorithms.
+- [ ] layer_configuration_options.md: Detailed documentation of available layer types and configuration options.
+- [ ] training_best_practices.md: Best practices for training neural networks, including data preprocessing, hyperparameter tuning, and regularization techniques.
+- [ ] evaluation_metrics.md: Explanation of various evaluation metrics used to assess neural network performance.

skills/neural-network-builder/scripts/README.md

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+# Scripts
+
+Bundled resources for neural-network-builder skill
+
+- [ ] build_network.py: Script to build a neural network based on a given configuration.
+- [ ] train_network.py: Script to train a neural network using specified data and parameters.
+- [ ] evaluate_network.py: Script to evaluate the performance of a trained neural network.
+- [ ] visualize_network.py: Script to visualize the architecture and performance of a neural network.