What are Claude Code Skills?

Claude Code Skills are reusable prompt templates stored as markdown files in your project's .claude/skills/ directory. They let you codify best practices and common workflows into slash commands that Claude follows consistently.

How do I create a Claude Code Skill?

Create a markdown file in .claude/skills/ with instructions for Claude. The filename becomes the command name — for example, .claude/skills/code-review.md can be invoked with /code-review during a Claude Code session.

Can I share skills with my team?

Yes. Skills stored in .claude/skills/ can be committed to version control so every team member has access to the same standardized workflows. You can also organize skills into subdirectories by category.

What is the difference between project and global skills?

Project skills (.claude/skills/) are specific to the current project and are committed to git. Global skills (~/.claude/skills/) are personal skills available across all your projects.

scientific-packages

Community RegistryGeneralby Timothy Kassis

Collection of python scientific packages

About this Skill

This skill equips developers with structured scientific thinking methodologies to analyze problems, form hypotheses, and design experiments. It helps you apply the scientific method to debugging, optimization, and decision-making in code projects, leading to more rigorous and reproducible outcomes.

Install & Usage

Create the skills directory

mkdir -p .claude/skills

Download the skill file

mkdir -p .claude/skills && curl -o .claude/skills/scientific-packages.md https://raw.githubusercontent.com/Juancho032007/claude-scientific-skills/main/SKILL.md

Invoke in Claude Code

/scientific-packages

Use Cases

Formulate and test hypotheses when debugging a complex system failure.

Design controlled experiments to compare performance of alternative algorithms.

Apply root cause analysis to identify the underlying issue in a recurring bug.

Use statistical reasoning to evaluate A/B test results for feature changes.

Structure a research approach for investigating a new library or framework.

Document and communicate experimental findings clearly for team review.

Usage Examples

/scientific-thinking I'm seeing intermittent timeouts in my microservice. Help me form a hypothesis and design an experiment to isolate the cause.

/scientific-thinking We need to decide between two caching strategies. Outline a controlled experiment with metrics to compare them.

/scientific-thinking My machine learning model's accuracy dropped after retraining. Walk me through a root cause analysis using scientific method.

View source on GitHub

python

Frequently Asked Questions

What is scientific-packages?

How to install scientific-packages?

To install scientific-packages, create the .claude/skills directory in your project, then run the curl command to download the skill file. Once installed, invoke it in Claude Code with /scientific-packages.

What is scientific-packages best for?

scientific-packages is a community categorized under General. It is designed for: python. Created by Timothy Kassis.

What can I use scientific-packages for?

scientific-packages is useful for: Formulate and test hypotheses when debugging a complex system failure.; Design controlled experiments to compare performance of alternative algorithms.; Apply root cause analysis to identify the underlying issue in a recurring bug.; Use statistical reasoning to evaluate A/B test results for feature changes.; Structure a research approach for investigating a new library or framework.; Document and communicate experimental findings clearly for team review..