project-scaffold

Automatically generates standard directory structures for different types of projects (data analysis, machine learning/deep learning, statistical modeling, and AI agents). Use this skill whenever a user wants to start a new project and needs to quickly create a standardized folder structure for their specific task.

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Install skill "project-scaffold" with this command: npx skills add biniguni/my-agent-skills/biniguni-my-agent-skills-project-scaffold

Project Scaffold Generator

This skill provides a standardized way to generate directory structures for new projects, ensuring consistency across different types of workflows (e.g., Data Analysis, ML/DL, Statistical Modeling, and Agent development).

When to use

Use this skill when a user asks to:

  • "Create a new folder structure for data analysis"
  • "Initialize an ML project"
  • "Set up directories for an AI agent"
  • "Make a skeleton for statistical modeling"

Available Task Types

The scaffold generator supports the following task types:

  • data_analysis: For EDA, data engineering, and general data analysis pipelines.
  • ml_dl: For machine learning and deep learning research and experiments.
  • stat_modeling: For statistical inference, A/B testing, and hypothesis testing.
  • agent: For building AI agents and their tools/memory systems.

For the complete recommended file and folder layouts of each task type, load the corresponding reference document:

How to use

Run the scaffold.py script to generate the necessary directories.

# General syntax
python scripts/scaffold.py <project_folder_name> --task <task_type>

# Example: Generating a data analysis structure in current directory
python scripts/scaffold.py my_data_project --task data_analysis

# Example: Generating an ML/DL structure in a specific path
python scripts/scaffold.py my_ml_project --task ml_dl --path ./my_projects_folder

Note: The script will only create the fundamental empty directory structure and .gitkeep files. It will not auto-generate boilerplate code files (main.py, train.py, .ipynb, etc.), allowing the user to create files only when they actually need them. You can show them the recommended file structure from the reference files if requested.

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