Optimization.experiment Brief

Purpose

Ensure experiments are well-defined, measurable, and aligned with user experience considerations before launch.

Pre-run Checklist

• ✅ Align with analytics on measurement feasibility and sample size.
• ✅ Confirm design assets and engineering bandwidth for variants.
• ✅ Review related research or previous experiments for context.

Invocation Guidance

codex run --skill optimization.experiment_brief \
  --vars "hypothesis={{hypothesis}}" \
         "primary_metric={{primary_metric}}" \
         "secondary_metrics={{secondary_metrics}}" \
         "audience={{audience}}"

Recommended Input Attachments

• Design mockups or copy variations.
• Experiment backlog or learning agenda.
• Prior experiment analyses.

Claude Workflow Outline

1. Summarize hypothesis, audience, and metrics.
2. Detail the experiment design: variants, allocation, instrumentation, and run duration.
3. Provide sample size estimation guidance and data dependencies.
4. Outline monitoring plan, success criteria, and decision framework.
5. Document collaboration and approval workflow.

Output Template

## Experiment Overview
- Hypothesis:
- Audience:
- Primary Metric:
- Secondary Metrics:

## Test Design
| Variant | Description | % Allocation | Key Changes |
| --- | --- | --- | --- |
- Expected Duration:
- Sample Size Estimate:

## Measurement & Monitoring
- Instrumentation Checklist:
- Data Quality Checks:
- Decision Cadence:

## Launch Plan
- Approvals:
- Launch Date:
- Responsibilities:

Follow-up Actions

• Secure approvals from product, design, engineering, and analytics leads.
• Schedule mid-test reviews to monitor guardrails.
• Plan post-test readout session.