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experiment-design

Design rigorous scientific experiments: define variables, controls, sample sizes, statistical power, protocols, expected outcomes, failure modes, and…

Skill by science-agent

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/suhyeong10-experiment-design

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About

Design rigorous scientific experiments: define variables, controls, sample sizes, statistical power, protocols, expected outcomes, failure modes, and confounders for biology, chemistry, and materials science experiments.

Use cases: design, research

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/experiment-design

What's inside

Skill name
experiment-design
Description
Design rigorous scientific experiments: define variables, controls, sample sizes, statistical power, protocols, expected outcomes, failure modes, and confounders for biology, chemistry, and materials science experiments.
License
MIT
Metadata
author: science-agent · version: 1.0
Instructions
50 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Experiment Design Skill

Use this skill when the user asks to design an experiment or validate a hypothesis.

## Required Sections for Every Experiment Design

### 1. Research Question
State the specific, testable hypothesis.

### 2. Variables
- **Independent variable**: What you manipulate
- **Dependent variable**: What you measure  
- **Control variables**: What you keep constant

### 3. Controls
- Positive control: confirms the assay works
- Negative control: confirms no false positives
- Vehicle/solvent control if applicable

### 4. Sample Size & Power
- Minimum detectable effect size
- Desired statistical power (typically 80%)
- α level (typically 0.05)
```python
from scipy.stats import norm
import numpy as np
def sample_size(effect_size, power=0.8, alpha=0.05):
    z_alpha = norm.ppf(1 - alpha/2)
    z_beta = norm.ppf(power)
    return int(np.ceil(2 * ((z_alpha + z_beta) / effect_size) ** 2))
```

### 5. Protocol Steps
Step-by-step reproducible procedure.

### 6. Expected Results
- If hypothesis is correct: ...
- If hypothesis is wrong: ...
- What would be ambiguous: ...

### 7. Confounders
List potential confounders and how to address each.

### 8. Failure Modes
What could go wrong and how to detect it.

### 9. Statistical Analysis Plan
Pre-specify the statistical test before running the experiment.

From suhyeong10/Science-Agent, MIT.

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Last commit (this folder)
Apr 22, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

experiment-design skill. Agent Mart. https://agentmart.dev/skills/suhyeong10-experiment-design (last updated 2026-10-24)