Experiment Design
SkillMonitoring & opsDesign robust experiments to test product hypotheses. Define metrics, sample size, and success criteria.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Experiment Design skill
What this skill tells your AI
The instructions your AI receives, as published by itseffi/agentic-os in .agents/skills/experiment-design/SKILL.md and read by ahel’s review.
Design robust experiments to test product hypotheses.
When to Use
When you have a hypothesis and need to design an experiment to validate it.
The Process
1. Factor Breakdown
Analyze your goal and system:
- What factors could influence the outcome?
- Which are controllable vs. environmental?
- What are the key variables?
2. Experiment Structure
For each factor, define:
- Hypothesis: What you expect to happen
- Independent variable: What you're changing
- Dependent variable: What you're measuring
- Control group: Baseline comparison
- Experimental group: Who gets the change
- Measurement method: How you'll collect data
- Confounding variables: What else could affect results
3. Sample Design
- Who participates?
- How many needed for statistical significance?
- How will you recruit/select?
4. Timeline
- How long to run?
- When to check results?
- What's the minimum detectable effect?
5. Stop/Scale Rules
- What results mean "stop"?
- What results mean "scale"?
- What's inconclusive?
Output Format
For each experiment:
- Hypothesis statement
- Variables (independent, dependent)
- Groups (control, experimental)
- Sample size and selection
- Measurement approach
- Success criteria
- Stop/scale rules
When Not to Use
Do not use this skill when the request is unrelated, low-stakes, or better handled by a simpler direct response.
Signals
- GitHub stars
- 112
- Forks
- 21
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
experiment-design-itseffi- Source
- github.com/itseffi/agentic-os