[PROJECT_NAME]

[Brief description of the project.]

Quick Start

1. Clone and enter the project

git clone <repo-url>
cd [PROJECT_NAME]

2. Set up Claude Code tools (RTK + jCodemunch)

Run once per user account (safe to re-run):

bash setup_claude_tools.sh

This installs:

• uv — fast Python package manager (replaces conda/pip for new projects)
• RTK — Rust Token Killer, reduces Claude token usage 60-90%
• jCodemunch-MCP — fast codebase indexing for Claude Code exploration

Options:

bash setup_claude_tools.sh --skip-rtk   # skip RTK
bash setup_claude_tools.sh --skip-mcp   # skip jCodemunch
bash setup_claude_tools.sh --skip-uv    # skip uv

3. Create the Python environment

# uv (recommended for new projects)
uv sync                    # creates .venv and installs from pyproject.toml
source .venv/bin/activate  # or: uv run python script.py

# conda (legacy)
conda env create -n [ENV_NAME] -f environment.yml
conda activate [ENV_NAME]

4. Configure paths

Edit code/utils/config.py and update:

• BASE_DIR — path to this project
• DATASETS_DIR — location of raw data
• SCRATCH_DIR — scratch space for intermediate files

5. Index the project in Claude Code

Open Claude Code in this directory and ask:

"Index this project"

Claude will call index_folder via jCodemunch-MCP. Subsequent code exploration uses symbol search instead of file reads — much faster and cheaper.

Project Structure

[PROJECT_NAME]/
├── code/
│   ├── analysis/          # Core analysis scripts
│   ├── notebooks/         # Jupyter notebooks
│   ├── plot/              # Plotting scripts
│   ├── stats/             # Statistical analysis (Python/R)
│   ├── submit_scripts/    # HPC job scripts (optional — SLURM/dSQ)
│   │   ├── dsq/           # DSQ shell scripts
│   │   └── joblists/      # Job lists (one command per line)
│   └── utils/
│       └── config.py      # Centralized path configuration
├── datasets/              # Symlinks or metadata for raw data (gitignored)
├── derivatives/           # Analysis outputs (gitignored)
│   ├── results/
│   └── logs/
├── pyproject.toml         # Python dependencies (uv)
├── setup_claude_tools.sh  # One-time environment setup
└── CLAUDE.md              # Instructions for Claude Code

Dependencies

Managed via uv. Edit pyproject.toml to add packages, then:

uv add numpy scipy pandas     # add dependencies
uv sync                       # sync environment
uv run python script.py       # run without activating venv

HPC Usage (optional)

If running on an HPC cluster with SLURM and dSQ, place job scripts in code/submit_scripts/. Skip this section if running locally or on a different scheduler.

# Submit a job array
sbatch code/submit_scripts/dsq/dsq_[analysis].sh

# Monitor
dsqa -j [JOB_ID]
squeue -u $USER

Claude Code Tips

• Use rtk git status, rtk git diff, etc. for compact output
• Use rtk gain to see token savings
• Run /mcp in Claude Code to verify jCodemunch is connected
• Ask Claude "Find the [function_name] function" — it uses symbol search, not file reads