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setup-squidpy-remote-tool

Set up, launch, validate, and troubleshoot the Squidpy ToolUniverse remote tool and optionally relay it through ToolUniverse Connect. Use when deploying or auditing this implementation.

skills/setup-squidpy-remote-tool/SKILL.md

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# Set up Squidpy as a remote tool

> **Sharing this model for a user, not developing it?** Follow the `tooluniverse-share-computer` guide; no ToolUniverse source checkout is needed. The model runs in its own Python environment, with its dependencies:
>
> ```bash
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/squidpy
> curl -fsSL -o ~/.tooluniverse/envs/squidpy-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt
> curl -fsSL -o ~/.tooluniverse/envs/squidpy-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/squidpy/requirements.txt
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/squidpy/bin/python -r ~/.tooluniverse/envs/squidpy-tooluniverse.txt -r ~/.tooluniverse/envs/squidpy-requirements.txt
> ~/.local/bin/tu remote check squidpy --python ~/.tooluniverse/envs/squidpy/bin/python   # lists anything still missing
> ~/.local/bin/tu serve --allow squidpy --python ~/.tooluniverse/envs/squidpy/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com
> ```
>
> If the check reports a missing environment variable (a data folder, a model cache, a provider key), set it in the same shell before `tu serve`; "Obtain credentials, data, and model weights" below says what each one is. The model reads only the variables it declares.