Experimental Design Planner
Plans study layouts: randomization, blocking, factorial and response-surface designs, seeded allocation schedules.
- Repository
- K-Dense-AI/scientific-agent-skills
- Stars
- 55,780
- Licence
- MIT
- Files
- 7 files
What it does
Once installed, the agent helps you plan a study before any data is collected. It chooses a suitable design type (randomized, blocked, factorial, crossover, cluster, response-surface, sequential) for your question and units, explains how to avoid confounding and pseudoreplication, and can generate reproducible, seeded allocation tables or design matrices in real factor units using bundled Python scripts.
Great for
- Researchers or students planning a lab, field, or clinical comparison and deciding how to assign subjects or samples to groups
- Engineers or scientists screening many factors or optimizing settings with a factorial or response-surface design
- Anyone who needs a documented, seeded randomization schedule for an auditable allocation
Skip it if
- Computing sample size or statistical power (the skill points to a separate statistical-power skill for that)
- Analyzing data that has already been collected (the skill points to statistical-analysis for that); design choices cannot rescue a confounded design after the fact
- Running a live enrollment or concealed allocation system for a real trial; the scripts explicitly do not implement enrollment or concealment, so a formal randomization service is needed for regulated trials
Try asking
- “How should I set up an experiment comparing three fertilizers across four fields without confounding?”
- “What's the best way to test these 6 factors with as few runs as possible?”
- “Assign these 60 mice to control and treatment groups in blocks of 4, with a reproducible seed.”
- “Generate a Latin hypercube sample of 50 settings for my simulation model over these parameter ranges.”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 7 original files, the MIT licence and an attribution note.
experimental-design.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip experimental-design.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip experimental-design.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Describe your units, nuisance factors (day, batch, site), and what you want to learn; the agent will pick the design branch from that rather than a favourite design.
- Choose and record your own seed for real studies; the default seed is for demonstration only, and archive package versions with the exported schedule so the output can be reproduced.
- Review the generated layout yourself, especially partial final blocks, which do not keep the requested ratio, before using it in a study.
What's inside
- references/design_types.md
- references/factorial_and_doe.md
- references/randomization_and_blocking.md
- references/sequential_and_adaptive.md
- scripts/doe_designs.py
- scripts/randomization.py
- SKILL.md
Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.