By yallup
Scientific Python development tools for JAX, plotting, and code review
Creates publication-quality scientific plots. Use when making plots, figures, or visualizations. Applies tueplots TMLR styling, LaTeX rendering, no titles.
Reviews code and executes tasks with senior engineer discipline. Use when reviewing code, implementing features, fixing bugs, or any coding task. Enforces minimal changes and production-safe practices.
Runs Python scripts and manages projects using uv. Use when asked to run a python script, execute python code, run pytest, or install packages. Always uses uv instead of pip or bare python.
Applies JAX patterns for scientific Python development. Use when working with JAX, distrax, numpyro, blackjax, or scientific computing. Covers vmap, JIT, RNG handling.
Skills and commands for scientific Python development.
Within Claude Code install directly with
/plugin marketplace add [email protected]:yallup/claude-skills.git
then add the plugins
/plugin install claude-skills@yallup
git clone https://github.com/yallup/claude-skills.git
Then in Claude Code:
/plugin marketplace add ./claude-skills
/plugin install claude-skills@yallup
python3 -c "
import json; from pathlib import Path
p = Path.home() / '.claude' / 'settings.json'
s = json.loads(p.read_text()) if p.exists() else {}
s.setdefault('permissions', {}).setdefault('allow', []).append('Skill(*)') if 'Skill(*)' not in s.get('permissions', {}).get('allow', []) else None
p.write_text(json.dumps(s, indent=2))
"
| Skill | Description |
|---|---|
reviewing-code | Senior engineer task execution |
using-jax | JAX patterns: JIT, vmap, RNG |
running-python | Runs Python with uv |
plotting | Publication-quality plots with tueplots |
Skills auto-activate based on context.
| Command | Description |
|---|---|
/code-review | Invoke reviewing-code with Python skill reminders |
cd claude-skills && git pull
MIT
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npx claudepluginhub yallup/claude-skills --plugin claude-skillsAgents and skills for Scientific Python development and best practices
Claude skills for Sciris features covering arrays, containers, file I/O, plotting, parallelization, dates, printing, utilities, and advanced features
Scientific research agent extension - turns research goals into reproducible Jupyter notebooks with Python REPL, data analysis, and ML workflows
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook. Originally from OpenAI's curated skills catalog.
Development automation skills for Python projects
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.