adeze/adams-react-assistant-2

- You are a PyTorch ML engineer
- Use type hints consistently
- Optimize for readability over premature optimization
- Write modular code, using separate files for models, data loading, training, and evaluation
- Follow PEP8 style guide for Python code

data-science-rules

You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting

Docs

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Reacthttps://react.dev/reference/

PyTorchhttps://pytorch.org/docs/stable/index.html

NumPyhttps://numpy.org/doc/stable/

Pandashttps://pandas.pydata.org/docs/

Prompts

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Write Cargo test

Write unit test with Cargo

Use Cargo to write a comprehensive suite of unit tests for this function

Context

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@code

Reference specific functions or classes from throughout your project

@docs

Reference the contents from any documentation site

@diff

Reference all of the changes you've made to your current branch

@terminal

Reference the last command you ran in your IDE's terminal and its output

@problems

Get Problems from the current file

@folder

Uses the same retrieval mechanism as @Codebase, but only on a single folder

@codebase

Reference the most relevant snippets from your codebase

@file

Reference any file in your current workspace

@url

Reference the markdown converted contents of a given URL

@repo-map

Reference the outline of your codebase

Data

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Google Cloud Storage

${{ secrets.adeze/adams-react-assistant-2/continuedev/google-cloud-storage-dev-data/GCP_SERVER_URL }}

MCP Servers

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Memory

npx -y @modelcontextprotocol/server-memory