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+# Contributing To MLRun's Hub
+
+## Types of Assets You Can Contribute
+- Modules (either a generic module or a model monitoring application)
+- Functions (will be converted to MLRun Runtime)
+
+## How to Contribute
+1. Fork this repository on GitHub and create a new branch for your new asset.
+2. Add a new directory for your asset under the appropriate directory (`functions/src` for functions, `modules/src` for modules).
+3. Populate the directory with your asset files (see the [Asset Structure](#asset-structure) section below).
+4. Open a pull request to merge your changes into the main repository, to its **development** branch.
+
+## Asset Structure
+### Functions
+```txt
+functions
+├── src
+│   ├── your_function_name
+│   │   ├── item.yaml
+│   │   ├── function.yaml
+│   │   ├── your_function_name.py
+│   │   ├── your_function_name.ipynb
+│   │   ├── test_your_function_name.py 
+│   │   └── requirements.txt
+```
+#### item.yaml 
+Metadata about the function. Can be generated using the following CLI command:
+  ```bash
+    python -m cli.cli generate-item-yaml function your_function_name
+  ```
+  Then, fill in all the relevant details. For example: `kind` (either `nuclio:serving`, `serving` or `job`) and `categories` field (you can browse the [MLRun hub UI](https://www.mlrun.org/hub/functions/) to see existing categories. You can specify more than one category per function).
+  Important: Be consistent with the module name across the directory name, all relevant `item.yaml` fields, and the file names.
+
+#### function.yaml
+The MLRun function definition. Can be generated from `item.yaml` using:
+```bash
+  python -m cli.cli item-to-function --item-path functions/src/your_function_name
+  ```
+#### your_function_name.py
+The main code file for your function. (Notice: keep the code well-documented, the docstrings are used in the hub UI as documentation for the function.)
+
+#### your_function_name.ipynb
+A Jupyter notebook demonstrating the function's usage. (Notice: the notebook must be able to run end-to-end automatically without manual intervention.)
+
+#### test_your_function_name.py
+Unit tests for your function to cover the function functionality as much as possible. (Will run upon each change to your function).
+
+#### requirements.txt
+Any additional Python dependencies required by your function's unit tests. (Notice: The function's own dependencies should be specified in the `item.yaml` file, not here.)
+
+### Modules
+```txt
+modules
+├── src
+│   ├── your_module_name
+│   │   ├── item.yaml
+│   │   ├── your_module_name.py
+│   │   ├── your_module_name.ipynb
+│   │   ├── test_your_module_name.py 
+│   │   └── requirements.txt
+```
+#### item.yaml
+Metadata about the module. Can be generated using the following CLI command:
+```bash
+  python -m cli.cli generate-item-yaml module your_module_name
+```
+Then, fill in all the relevant details. For example: `kind` (either `generic` or `monitoring_application`) and `categories` (you can browse the [MLRun hub UI](https://www.mlrun.org/hub/functions/) to see existing categories. You can specify more than one category per module).
+Important: Be consistent with the module name across the directory name, all relevant `item.yaml` fields, and the file names.
+
+#### your_module_name.py
+The main code file for your module. (Notice: keep the code well-documented, the docstrings are used in the hub UI as documentation for the module.)
+For model-monitoring modules, you can see our [guidelines for writing model monitoring applications](https://docs.mlrun.org/en/stable/model-monitoring/applications.html).
+
+#### your_module_name.ipynb
+A Jupyter notebook demonstrating the module's usage. 
+
+#### test_your_module_name.py
+Unit tests for your module to cover the module functionality as much as possible. (Will run upon each change to your module).
+
+#### requirements.txt
+Any additional Python dependencies required by your module's unit tests. (Notice: The module's own dependencies should be specified in the `item.yaml` file, not here.)
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