experiment-design
Design rigorous scientific experiments: define variables, controls, sample sizes, statistical power, protocols, expected outcomes, failure modes, and…
Skill by science-agent
Install
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
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, research
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
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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Signals
- Stars
- 0
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Apr 22, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0
- License
- MIT
- Repository
- github.com/suhyeong10/Science-Agent
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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astronomy
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Cite this page
experiment-design skill. Agent Mart. https://agentmart.dev/skills/suhyeong10-experiment-design (last updated 2026-10-24)