Overview
experimental-design is an open-source data skill for Claude Code and compatible agents, published by K-Dense-AI. Its author describes it as: “Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks ho…”. The project has 34k stars on GitHub and is available under the MIT license. Add it to your setup with `git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/experimental-design`.
What experimental-design does
The design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made *before* data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.
What’s inside
The SKILL.md for experimental-design is organised into these sections:
- Overview
- When to Use This Skill
- Installation
- Choosing a design
- Generating the design
- Randomization / allocation schedules — scripts/randomization.py
- DOE matrices — scripts/doedesigns.py
- The mistakes that ruin studies
- Workflow
- Resources
- Scripts
- References
Installation
Add experimental-design to your agent with:
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/experimental-designReview a skill’s source before installing it — skills can instruct your agent to run commands. This command comes from the skill’s public repository, which is the source of truth for setup steps. New to skills? Read how to install Claude Code skills.
Supported agents
Neither the skill nor its repository names a specific agent. It uses the SKILL.md format, which several agents can load — check the repository for any agent-specific requirements.
License
experimental-design is published under the MIT license.
Strengths
- Clear MIT license — safe to read and adapt within its terms
- Ships in K-Dense-AI/scientific-agent-skills, an established project with 33,821 GitHub stars
- Actively maintained — repository updated in the last 90 days
Author
Topics
Frequently asked questions
- What does experimental-design do?
- Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
- How do I install experimental-design?
- Run git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/experimental-design in your agent, then reload your skills. Review the source at https://github.com/K-Dense-AI/scientific-agent-skills before installing.
- Is experimental-design free to use?
- Yes. experimental-design is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does experimental-design come from?
- experimental-design ships inside K-Dense-AI/scientific-agent-skills, a repository that contains 42 catalogued skills in total. The repository's 33,821 GitHub stars apply to that whole collection, not to this skill on its own.