[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F7VJFNSXAFXDF093T5YXH9":3,"skill-same:01M6F7VJFNSXAFXDF093T5YXH9":80},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6F7VJFNSXAFXDF093T5YXH9","building-neural-networks",null,"Execute this skill allows AI assistant 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... 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Trigger with relevant phrases based on skill purpose.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fbuilding-neural-networks\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"neural networks","networks","Building Neural Networks","Neural","edit","relevant","architectures","neural network","io","creation","builder","context","assistant","write","read","configure","Bash","network","code","openclaw","2026-11-10 10:12:30",false,"---\nname: building-neural-networks\ndescription: |\n  Execute this skill allows AI assistant 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... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)\nversion: 1.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\ncompatible-with: claude-code, codex, openclaw\ntags: [ai, neural-networks]\n\n---\n# Neural Network Builder\n\nDesign and construct neural network architectures with configurable layers, activation functions, and training parameters for tasks like classification, generation, and sequence modeling.\n\n## Overview\n\nThis 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.\n\n## How It Works\n\n1. **Analyzing Requirements**: Claude analyzes the user's request to understand the desired neural network architecture, task, and performance goals.\n2. **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.\n3. **Executing Build**: Claude executes the `build-nn` command, triggering the neural-network-builder plugin to construct the neural network based on the generated configuration.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Create a new neural network architecture for a specific machine learning task.\n- Modify an existing neural network's layers, parameters, or training process.\n- Design a neural network using specific layer types, such as convolutional, recurrent, or transformer layers.\n\n## Examples\n\n### Example 1: Image Classification\n\nUser request: \"Build a convolutional neural network for image classification with three convolutional layers and two fully connected layers.\"\n\nThe skill will:\n1. Analyze the request and determine the required CNN architecture.\n2. Generate the configuration for the `build-nn` command, specifying the layer types, filter sizes, and activation functions.\n\n### Example 2: Text Generation\n\nUser request: \"Define an RNN architecture for text generation with LSTM cells and an embedding layer.\"\n\nThe skill will:\n1. Analyze the request and determine the required RNN architecture.\n2. Generate the configuration for the `build-nn` command, specifying the LSTM cell parameters, embedding dimension, and output layer.\n\n## Best Practices\n\n- **Layer Selection**: Choose appropriate layer types (e.g., convolutional, recurrent, transformer) based on the task and data characteristics.\n- **Parameter Tuning**: Experiment with different parameter values (e.g., learning rate, batch size, number of layers) to optimize performance.\n- **Regularization**: Implement regularization techniques (e.g., dropout, L1\u002FL2 regularization) to prevent overfitting.\n\n## Integration\n\nThis 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.\n\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands\n","# Neural Network Builder\n\nDesign and construct neural network architectures with configurable layers, activation functions, and training parameters for tasks like classification, generation, and sequence modeling.\n\n## Overview\n\nThis 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.\n\n## How It Works\n\n1. **Analyzing Requirements**: Claude analyzes the user's request to understand the desired neural network architecture, task, and performance goals.\n2. **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.\n3. **Executing Build**: Claude executes the `build-nn` command, triggering the neural-network-builder plugin to construct the neural network based on the generated configuration.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Create a new neural network architecture for a specific machine learning task.\n- Modify an existing neural network's layers, parameters, or training process.\n- Design a neural network using specific layer types, such as convolutional, recurrent, or transformer layers.\n\n## Examples\n\n### Example 1: Image Classification\n\nUser request: \"Build a convolutional neural network for image classification with three convolutional layers and two fully connected layers.\"\n\nThe skill will:\n1. Analyze the request and determine the required CNN architecture.\n2. Generate the configuration for the `build-nn` command, specifying the layer types, filter sizes, and activation functions.\n\n### Example 2: Text Generation\n\nUser request: \"Define an RNN architecture for text generation with LSTM cells and an embedding layer.\"\n\nThe skill will:\n1. Analyze the request and determine the required RNN architecture.\n2. Generate the configuration for the `build-nn` command, specifying the LSTM cell parameters, embedding dimension, and output layer.\n\n## Best Practices\n\n- **Layer Selection**: Choose appropriate layer types (e.g., convolutional, recurrent, transformer) based on the task and data characteristics.\n- **Parameter Tuning**: Experiment with different parameter values (e.g., learning rate, batch size, number of layers) to optimize performance.\n- **Regularization**: Implement regularization techniques (e.g., dropout, L1\u002FL2 regularization) to prevent overfitting.\n\n## Integration\n\nThis 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.\n\n## Prerequisites\n\n- Appropriate file access permissions\n- Required dependencies installed\n\n## Instructions\n\n1. Invoke this skill when the trigger conditions are met\n2. Provide necessary context and parameters\n3. Review the generated output\n4. Apply modifications as needed\n\n## Output\n\nThe skill produces structured output relevant to the task.\n\n## Error Handling\n\n- Invalid input: Prompts for correction\n- Missing dependencies: Lists required components\n- Permission errors: Suggests remediation steps\n\n## Resources\n\n- Project documentation\n- Related skills and commands\n",{"name":5,"tags":49,"author":52,"license":15,"version":53,"description":54,"allowed-tools":55,"compatible-with":56},[50,51],"ai","neural-networks","Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Execute this skill allows AI assistant 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... Use when appropriate context detected. 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