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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\u002Fsplitting-datasets\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"Splitting Datasets","datasets","write","with","relevant","dataset","trigger","io","use","edit","testing","read","model","Bash","data","code","development","training","ml","openclaw","2026-11-10 10:12:30",false,"---\nname: splitting-datasets\ndescription: |\n  Process split datasets into training, validation, and testing sets for ML model development. Use when requesting \"split dataset\", \"train-test split\", or \"data partitioning\". 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, testing, ml]\n\n---\n# Dataset Splitter\n\nSplit datasets into training, validation, and testing sets with configurable ratios and stratification options.\n\n## Overview\n\nThis skill automates the process of dividing a dataset into subsets for training, validating, and testing machine learning models. It ensures proper data preparation and facilitates robust model evaluation.\n\n## How It Works\n\n1. **Analyze Request**: The skill analyzes the user's request to determine the dataset to be split and the desired proportions for each subset.\n2. **Generate Code**: Based on the request, the skill generates Python code utilizing standard ML libraries to perform the data splitting.\n3. **Execute Splitting**: The code is executed to split the dataset into training, validation, and testing sets according to the specified ratios.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Prepare a dataset for machine learning model training.\n- Create training, validation, and testing sets.\n- Partition data to evaluate model performance.\n\n## Examples\n\n### Example 1: Splitting a CSV file\n\nUser request: \"Split the data in 'my_data.csv' into 70% training, 15% validation, and 15% testing sets.\"\n\nThe skill will:\n1. Generate Python code to read the 'my_data.csv' file.\n2. Execute the code to split the data according to the specified proportions, creating 'train.csv', 'validation.csv', and 'test.csv' files.\n\n### Example 2: Creating a Train-Test Split\n\nUser request: \"Create a train-test split of 'large_dataset.csv' with an 80\u002F20 ratio.\"\n\nThe skill will:\n1. Generate Python code to load 'large_dataset.csv'.\n2. Execute the code to split the dataset into 80% training and 20% testing sets, saving them as 'train.csv' and 'test.csv'.\n\n## Best Practices\n\n- **Data Integrity**: Verify that the splitting process maintains the integrity of the data, ensuring no data loss or corruption.\n- **Stratification**: Consider stratification when splitting imbalanced datasets to maintain class distributions in each subset.\n- **Randomization**: Ensure the splitting process is randomized to avoid bias in the resulting datasets.\n\n## Integration\n\nThis skill can be integrated with other data processing and model training tools within the Claude Code ecosystem to create a complete machine learning workflow.\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","# Dataset Splitter\n\nSplit datasets into training, validation, and testing sets with configurable ratios and stratification options.\n\n## Overview\n\nThis skill automates the process of dividing a dataset into subsets for training, validating, and testing machine learning models. It ensures proper data preparation and facilitates robust model evaluation.\n\n## How It Works\n\n1. **Analyze Request**: The skill analyzes the user's request to determine the dataset to be split and the desired proportions for each subset.\n2. **Generate Code**: Based on the request, the skill generates Python code utilizing standard ML libraries to perform the data splitting.\n3. **Execute Splitting**: The code is executed to split the dataset into training, validation, and testing sets according to the specified ratios.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Prepare a dataset for machine learning model training.\n- Create training, validation, and testing sets.\n- Partition data to evaluate model performance.\n\n## Examples\n\n### Example 1: Splitting a CSV file\n\nUser request: \"Split the data in 'my_data.csv' into 70% training, 15% validation, and 15% testing sets.\"\n\nThe skill will:\n1. Generate Python code to read the 'my_data.csv' file.\n2. Execute the code to split the data according to the specified proportions, creating 'train.csv', 'validation.csv', and 'test.csv' files.\n\n### Example 2: Creating a Train-Test Split\n\nUser request: \"Create a train-test split of 'large_dataset.csv' with an 80\u002F20 ratio.\"\n\nThe skill will:\n1. Generate Python code to load 'large_dataset.csv'.\n2. Execute the code to split the dataset into 80% training and 20% testing sets, saving them as 'train.csv' and 'test.csv'.\n\n## Best Practices\n\n- **Data Integrity**: Verify that the splitting process maintains the integrity of the data, ensuring no data loss or corruption.\n- **Stratification**: Consider stratification when splitting imbalanced datasets to maintain class distributions in each subset.\n- **Randomization**: Ensure the splitting process is randomized to avoid bias in the resulting datasets.\n\n## Integration\n\nThis skill can be integrated with other data processing and model training tools within the Claude Code ecosystem to create a complete machine learning workflow.\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":52,"author":54,"license":15,"version":55,"description":56,"allowed-tools":57,"compatible-with":58},[53,37,45],"ai","Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Process split datasets into training, validation, and testing sets for ML model development. Use when requesting \"split dataset\", \"train-test split\", or \"data partitioning\". 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