doc-comments-ai is an LLM-powered tool designed to automate code documentation generation, enhancing clarity and consistency in codebases. It supports various documentation formats, making it versatile for developers across multiple programming languages.
claude install fynnfluegge/doc-comments-aidoc-comments-ai is an LLM-powered tool designed to automate code documentation generation, enhancing clarity and consistency in codebases. It supports various documentation formats, making it versatile for developers across multiple programming languages.
Generate docstrings for Python functions
Create Javadoc for Java projects
Automate documentation for Rust codebases
Enhance existing documentation with AI suggestions
claude install fynnfluegge/doc-comments-aigit clone https://github.com/fynnfluegge/doc-comments-aiCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate comprehensive docstring comments for the following Python function: [FUNCTION_CODE]. Ensure the comments follow the NumPy docstring format, including a description, parameters, return values, and any relevant examples. Focus on making the documentation clear and useful for other developers who might use this function.
Here is an example of the AI-generated docstring for a Python function that calculates the moving average of a dataset:
```python
def calculate_moving_average(data, window_size):
"""
Calculate the moving average of a dataset using a specified window size.
Parameters
----------
data : list
A list of numerical values representing the dataset.
window_size : int
The size of the window over which to calculate the moving average.
Returns
-------
list
A list of moving averages corresponding to the input data.
Examples
--------
>>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> window_size = 3
>>> calculate_moving_average(data, window_size)
[2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
"""
if window_size < 1:
raise ValueError("Window size must be at least 1")
moving_averages = []
for i in range(len(data) - window_size + 1):
window = data[i:i + window_size]
moving_averages.append(sum(window) / window_size)
return moving_averages
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