Create a New Project

When the user invokes /new-project, scaffold a complete project with version control and Claude Code configuration. The project type determines which conventions, directories, and environments to set up.

Run this skill from inside the target project directory (which may be empty or newly created).

1. Gather Project Information

Use AskUserQuestion to collect:

Question 1: Project type

•Data science (Recommended) — Analysis project with numbered scripts, data/outs/ directories, renv/conda, Quarto analysis documents
•Documentation — Quarto book or website (teaching material, lab manual, reference docs)
•General — Any other project (infrastructure, tools, packages, scripts without data science conventions)

Question 2: Project basics (all types)

•Project name: Default to current directory name
•Brief description: 1-2 sentences for CLAUDE.md and README

For Data Science projects, also ask:

Question 3: Languages

•R only
•Python only
•Both R and Python (Recommended)

Question 4: Layout

•Flat: Single scripts/ directory — for small projects with <10 scripts on one topic
•Sectioned: Subdirectories under scripts/, data/, outs/ — for larger projects with multiple analytical threads

If sectioned, ask for section names (e.g., phosphoproteomics, transcriptomics).

For General projects, also ask:

Question 3: Languages

•R only
•Python only
•Both R and Python
•None / other (Markdown, Bash, etc.)

For all types:

Question: GitHub

•Lab organization (recommended for research projects) or personal account?
•Private (recommended) or public?

Question: Changelog

•Yes (recommended) — create a CHANGELOG.md to track changes over time. The /done skill will propose entries automatically.
•No — skip. You can always add one later.

2. Create Directory Structure

Data Science layout

Flat

bash

mkdir -p R python scripts/exploratory data outs/exploratory .claude

(Omit R/ if Python-only, omit python/ if R-only.)

Sectioned

For each section (e.g., phosphoproteomics, transcriptomics):

bash

mkdir -p R python scripts/exploratory data outs/exploratory .claude
# Per section:
mkdir -p scripts/{section} data/{section} outs/{section}

Create placeholder files

Add .gitkeep files to empty directories so git tracks them:

bash

touch R/.gitkeep python/.gitkeep scripts/exploratory/.gitkeep outs/.gitkeep data/.gitkeep

Documentation layout

Hand off to the /quarto-book-setup skill:

"This is a documentation project — I'll use /quarto-book-setup to scaffold the Quarto book. Would you like to proceed?"

After /quarto-book-setup completes, return here to add project-type: general to the generated CLAUDE.md (documentation projects use the general type since they don't follow data science conventions).

General layout

bash

mkdir -p .claude

Create only the .claude/ directory. Do NOT create data/, outs/, scripts/, R/, or python/ directories — let the user organize as appropriate for their project.

3. Python Environment (if using Python)

One-time conda configuration (check first)

bash

conda config --show channel_priority

If not already strict:

bash

conda config --set channel_priority strict
conda config --set solver libmamba
conda config --add channels conda-forge

For Data Science projects: create project-specific environment

bash

source ~/miniconda3/etc/profile.d/conda.sh
conda create -n {project_name} python=3.11 numpy pandas matplotlib ipykernel -y
conda activate {project_name}

Customize: Replace ~/miniconda3 with your actual conda installation path (see conda-env skill).

ipykernel is required for Quarto to execute Python chunks.

For General projects: default to `lab-general`

General projects always get the shared lab-general environment by default. This is included in the CLAUDE.md regardless of whether the project currently uses Python — it ensures Claude knows which env to activate if Python is needed later.

•
Check if the lab-general environment already exists:
bash
```
source ~/miniconda3/etc/profile.d/conda.sh && conda env list | grep lab-general
```
Customize: Replace ~/miniconda3 with your actual conda installation path (see conda-env skill).

•

If it does NOT exist, create it:

bash

source ~/miniconda3/etc/profile.d/conda.sh
conda create -n lab-general python=3.11 ipykernel pyyaml requests pandas -y

•
Do NOT export an environment.yml into the project — lab-general is managed independently, not per-project.

Opt-out: project-specific environment. If the user specifically says they need specialized Python dependencies, create a project-specific env instead:

•
Ask the user which packages to install.

•

Create the environment:

bash

source ~/miniconda3/etc/profile.d/conda.sh
conda create -n {project_name} python=3.11 ipykernel {user_packages} -y
conda activate {project_name}

•

Export environment:

bash

conda env export --from-history > environment.yml

Export environment (Data Science and project-specific General only)

Always use --from-history for portable environment files:

bash

conda env export --from-history > environment.yml

Do NOT create an environment.yml for projects using the shared lab-general environment.

Lab policy

•Never install into the base environment
•Data science projects: one environment per project, named to match the project
•General projects: use lab-general shared environment by default; create project-specific only when specialized dependencies are needed
•Prefer conda install over pip install
•Always include ipykernel for Quarto compatibility

4. R + renv (Data Science only — if using R)

Check available R versions

bash

rig list

Ask the user which version to use (default: latest installed).

Pin R version in Positron

Ask: "Do you use Positron as your IDE?"

If yes, create .positron/settings.json:

json

{
  "r.rpath.mac": "/Library/Frameworks/R.framework/Versions/{VERSION}-arm64/Resources/bin/R"
}

Replace {VERSION} with the chosen version (e.g., 4.4). For Intel Macs, use {VERSION}-x86_64.

Add .positron/ to .gitignore (already in the template).

Initialize renv

renv::init()
install.packages(c("tidyverse", "here"))
renv::snapshot()

Lab policy

•Every R data science project uses renv
•Snapshot after every package install
•Commit renv.lock, renv/activate.R, .Rprofile to git
•Do NOT commit renv/library/ or renv/staging/

5. Write .gitignore

Data Science .gitignore

code

# Generated outputs (reproducible from code)
outs/

# R artifacts
.Rhistory
.RData
.Rproj.user/
renv/library/
renv/staging/
renv/local/
*_cache/

# Python artifacts
__pycache__/
*.py[cod]
*.egg-info/
.eggs/
*.egg
.venv/
venv/

# Quarto rendering
*_files/
.quarto/
*.html

# Claude Code
.claude/worktrees/

# OS files
.DS_Store
Thumbs.db

# IDE settings
.vscode/
.positron/
*.Rproj

# Secrets
.env
*.pem
credentials.json

Note on data/: Whether to gitignore data/ depends on file sizes. Small data files (< a few MB) can be committed. Large files should be gitignored with a data/README.md documenting sources. Ask the user.

General .gitignore

code

# Python artifacts
__pycache__/
*.py[cod]
*.egg-info/
.venv/
venv/

# R artifacts
.Rhistory
.RData
.Rproj.user/

# Quarto rendering
*_files/
.quarto/

# Claude Code
.claude/worktrees/

# OS files
.DS_Store
Thumbs.db

# IDE settings
.vscode/
.positron/

# Secrets
.env
*.pem
credentials.json

Omit language-specific sections if that language isn't used.

6. Generate .claude/CLAUDE.md

Data Science template

Use this template, filling in project-specific details:

markdown

<!-- project-type: data-science -->
# {Project Name}

{Brief description from step 1}

---

## Environment

{Include only the sections relevant to the chosen languages}

- **Python/Conda**: `{project_name}` environment
  ```bash
  source ~/miniconda3/etc/profile.d/conda.sh && conda activate {project_name}
  ```
  Packages: Python 3.11, pandas, numpy, matplotlib, ipykernel

- **R packages**: Managed with renv (see user CLAUDE.md for general renv instructions)
  - R version: {version} (pinned in `.positron/settings.json`)

---

## Repository Layout

```
{project_name}/
  R/                          # Shared R helpers
  python/                     # Shared Python helpers
  scripts/
    {section dirs or flat}
    exploratory/              # One-off analyses
  data/                       # External inputs only — scripts never write here
  outs/                       # All script outputs
    {section dirs or flat}
    exploratory/
  .claude/
    CLAUDE.md                 # This file
```

Scripts use `here::here()` (R) or `PROJECT_ROOT` (Python) — run from repository root.

---

## Script Conventions

This project follows the conventions in the user-level `script-organization` and `quarto-docs` skills:
- Numbered with `XX_` prefix, matching `outs/XX_script_name/` folder
- Include `status: development` in YAML frontmatter
- Git hash in setup chunk, BUILD_INFO.txt at end
- Inputs grouped at top with comments (external vs project outputs)
- Use `source(here("R/..."))` (R) or `sys.path.insert` (Python) for shared helpers
- `.qmd` format for all data science scripts (both R and Python)
- `.py` files only for standalone utilities or CLI tools

---

## Key Files

(Add important files here as the project develops)

---

## Workflows

(Document how to run the analysis as it develops)

---

## Conventions

See user-level skills for detailed conventions:
- `script-organization` — directory structure, numbering, lifecycle, provenance
- `quarto-docs` — QMD templates (R and Python), rendering
- `data-handling` — data validation, analytical decisions
- `r-plotting-style` — ggplot2 theme
- `conda-env` — conda activation patterns
- `r-renv` — R package management
- `file-safety` — output ownership, data/ protection

---

## Project Document Registry

### Planning Documents

| Document | Topic | Has status table? |
|----------|-------|:-:|
| (add planning documents as work develops) | | |

### Data Documents

| Document | Topic |
|----------|-------|
| (add data documents as datasets are documented) | |

### Convention/Reference

| Document | Topic |
|----------|-------|
| [CLAUDE.md](.claude/CLAUDE.md) | Project conventions, environment, pipelines |

If ~/lib/R/ or ~/lib/python/ exist, add to the Conventions section:

markdown

### Cross-Project Helpers

Shared helper functions are available at:
- `~/lib/R/` — source with `source("~/lib/R/helpers.R")`
- `~/lib/python/` — import with `sys.path.insert(0, os.path.expanduser("~/lib/python"))`

General project template

markdown

<!-- project-type: general -->
# {Project Name}

{Brief description from step 1}

---

## Environment

- **Python/Conda**: Shared `lab-general` environment
  ```bash
  source ~/miniconda3/etc/profile.d/conda.sh && conda activate lab-general
  ```

> **Customize**: Replace `~/miniconda3` with your actual conda installation path.

{If project-specific environment was chosen instead, replace the above with:}
- **Python/Conda**: `{project_name}` environment
  ```bash
  source ~/miniconda3/etc/profile.d/conda.sh && conda activate {project_name}
  ```

---

## Repository Layout

```
{Describe the actual directory structure as it develops}
```

---

## Key Files

(Add important files here as the project develops)

---

## Workflows

(Document how to run things as the project develops)

---

## Project Document Registry

### Planning Documents

| Document | Topic | Has status table? |
|----------|-------|:-:|
| (add as work develops) | | |

### Convention/Reference

| Document | Topic |
|----------|-------|
| [CLAUDE.md](.claude/CLAUDE.md) | Project conventions |

Project reminders file

Create .claude/project-reminders.txt — this is read by the project-reminders hook to inject critical project-specific rules at every session start.

Data Science projects

code

CRITICAL REMINDERS (re-injected after compaction):
1. (Add project-specific rules here as the project develops)
2. Check planning documents before modifying scripts
3. Never silently default unmatched data classifications

General projects

code

REMINDERS (re-injected after compaction):
1. (Add project-specific rules here as the project develops)

Tell the user: "I created .claude/project-reminders.txt — edit this as you discover critical rules that Claude keeps forgetting after long sessions."

7. Generate README.md

Data Science README

markdown

# {Project Name}

{Brief description}

## Setup

### Prerequisites
- [Positron](https://positron.posit.co/) (recommended IDE)
- [rig](https://github.com/r-lib/rig) (R version manager)
- [Conda](https://docs.conda.io/) (Python environment manager)
- [Quarto](https://quarto.org/) (literate programming)

### Python
```bash
conda env create -f environment.yml
conda activate {project_name}
```

### R
```r
# renv auto-activates via .Rprofile
renv::restore()
```

## Data

(Document data sources and how to obtain them)

## Running the Analysis

(Document how to run scripts in order)

Omit Python or R sections if not using that language.

General README

markdown

# {Project Name}

{Brief description}

## Setup

{Document prerequisites and setup steps as the project develops}

## Usage

{Document how to use the project}

7b. Create CHANGELOG.md (if requested)

If the user opted for a changelog in Step 1, create CHANGELOG.md:

markdown

# Changelog

## {today's date}

### Added
- Initial project setup

The /done skill auto-detects this file and proposes entries at each session wrap-up. No CLAUDE.md entry needed.

8. Git + GitHub

bash

git init
git add .
git commit -m "Initial project setup"

Then create the remote:

bash

# Lab org, private (default)
gh repo create LAB_ORG/{project_name} --private --source=. --push

# Personal, private
gh repo create {project_name} --private --source=. --push

# Personal, public
gh repo create {project_name} --public --source=. --push

Customize: Replace LAB_ORG with your lab's GitHub organization name.

9. Summary

After completing all steps, print a type-appropriate summary:

Data Science summary

code

Project "{project_name}" created successfully!

  Project type: Data science
  Directory structure: {flat/sectioned}
  Languages: {R/Python/both}
  Conda env: {project_name}
  R version: {version} (renv initialized)
  Git remote: {github_url}

Next steps:
  1. Add data files to data/
  2. Create your first script: scripts/01_import.qmd
  3. See the quarto-docs skill for QMD templates

General summary

code

Project "{project_name}" created successfully!

  Project type: General
  Languages: {languages}
  Conda env: {lab-general (shared) / project_name (project-specific)}
  Git remote: {github_url}

Next steps:
  1. Start adding code and documentation
  2. Update .claude/CLAUDE.md as the project develops

Create a New Project

1. Gather Project Information

Question 1: Project type

Question 2: Project basics (all types)

For Data Science projects, also ask:

Question 3: Languages

Question 4: Layout

For General projects, also ask:

Question 3: Languages

For all types:

Question: GitHub

Question: Changelog

2. Create Directory Structure

Data Science layout

Flat

Sectioned

Create placeholder files

Documentation layout

General layout

3. Python Environment (if using Python)

One-time conda configuration (check first)

For Data Science projects: create project-specific environment

For General projects: default to lab-general

Export environment (Data Science and project-specific General only)

Lab policy

4. R + renv (Data Science only — if using R)

Check available R versions

Pin R version in Positron

Initialize renv

Lab policy

5. Write .gitignore

Data Science .gitignore

General .gitignore

6. Generate .claude/CLAUDE.md

Data Science template

General project template

Project reminders file

Data Science projects

General projects

7. Generate README.md

Data Science README

General README

7b. Create CHANGELOG.md (if requested)

8. Git + GitHub

9. Summary

Data Science summary

General summary

For General projects: default to `lab-general`