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Reproducible Python environments, notebooks, and literate programming
Passed 20 of 25 test cases with the skill, and 16 of 25 without it.
and 1 more in the full report
4 fixed17 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need command-line commands using jupyter nbconvert for four tasks on analysis.ipynb: 1) convert to a Python script, 2) execute the notebook in-place, 3) convert to an HTML report hiding code inputs, and 4) convert to PDF. What are the four commands?
| Checked | Without skill | With skill |
|---|---|---|
| Uses --to script to convert analysis.ipynb to a Python script | Passed | Passed |
| Uses --execute --to notebook --inplace to run analysis.ipynb | Missed | Passed |
| Uses --to html --no-input to create an HTML report hiding code inputs | Passed | Passed |
The instructions your model reads when the skill loads.
Set up reproducible Python environments for research computing, using virtual environments, dependency management, Jupyter notebooks, and literate programming practices.
# Option 1: venv (built-in, lightweight)
python -m venv .venv
source .venv/bin/activate # macOS/Linux
# .venv\Scripts\activate # Windows
pip install -r requirements.txt
# Option 2: conda (includes non-Python dependencies)
conda create -n myproject python=3.11
conda activate myproject
conda install numpy pandas scipy matplotlib
conda env export > environment.yml
# Option 3: uv (fast, modern Python package manager)
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
# requirements.txt with exact versions (pip freeze)
pip freeze > requirements.txt
# Better: use pip-tools for compiled dependencies
pip install pip-tools
# Create requirements.in (human-readable, loose constraints)
cat > requirements.in << 'EOF'
numpy>=1.24
pandas>=2.0
test cases passed, vs 16 / 25 without
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