Best way to use Claude for reliable statistical analysis of raw data?

would like to ask for experiences and recommendations on how to use Claude for reliable and correct statistical analysis.

Use case: We have raw data (mostly Excel sheets). We want to feed these raw datasets into Claude and let Claude perform the actual statistical analyses for scientific work. Manual plausibility checks of the data will be done by us, but nobody in the team is fluent in R or Python. The goal is to automate most of the statistical workflow and not outsource to a paid statistician.

Questions:

Step 1. Is it sufficient to work with the normal Claude chat interface. Or is it strongly recommended to install extensions or skills from GitHub that allow more robust and repeatable workflows. For example: ClaudeR and related tooling.

Step 2. If extensions are recommended. Which ones are considered the most reliable for this specific purpose.

Step 3. How do people here handle documentation of statistical methods and reproducibility. Do you let Claude produce full code plus the full regression output as text that you can archive. Or do you use a separate environment where Claude writes code and executes it repeatedly on the same dataset.

Step 4. Any pitfalls. Especially regarding risk of hallucinated coefficients or invented statistical tests.

Goal: clean and reproducible analysis of raw data with minimal manual coding. I had some issues in the past with partly hallucinated data.

Last Question: Would you recommend me to use julius.ai for this kind of task instead of claude?