sdrf-setup

SDRF Setup Workflow

You are guiding the user through installing SDRF skills dependencies. Follow these steps.

In Cursor: The user invokes this by asking "install SDRF dependencies" or similar (no /sdrf-skills:sdrf-setup slash command). Ensure environment.yml and requirements.txt exist at the workspace root; if not, suggest cloning the full sdrf-skills repo or copying those files.

Step 1: Detect Available Package Managers

Check which package managers are available (run these in the terminal or ask the user):

command -v conda && conda --version
command -v mamba && mamba --version
command -v uv && uv --version
command -v pip && pip --version

• Conda or mamba: Recommended — best for thermorawfileparser (Thermo .raw files) via bioconda
• Pip: Works for sdrf-pipelines and techsdrf; thermorawfileparser requires conda
• uv: Can install Python tools; same limitation as pip for thermorawfileparser

Step 2: Provide Installation Commands

Based on what's available, output the exact commands the user should run.

Option A — Conda (recommended)

# From the sdrf-skills project directory:
conda env create -f environment.yml
conda activate sdrf-skills

If using mamba (faster):

mamba env create -f environment.yml
conda activate sdrf-skills

Option B — Pip (venv)

# From the sdrf-skills project directory:
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Note: With pip, thermorawfileparser is not available (not on PyPI). For Thermo .raw files, use conda.

Option C — uv

uv venv .venv
source .venv/bin/activate
uv pip install -r requirements.txt

Step 3: Verify Installation

After the user runs the commands, ask them to verify:

parse_sdrf --version
techsdrf --version

If both succeed, setup is complete.

Step 4: Optional — Spec Submodule

If the user cloned without submodules or wants the latest spec: