send-experiment-designer
Design a reliable email experiment
Email teams can make costly decisions from underpowered or poorly controlled tests. This skill creates a structured experiment plan and interprets results using user-provided campaign data.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "send-experiment-designer" from https://skillstore.io/skills/aaron-he-zhu-send-experiment-designer.md and its manifest at https://skillstore.io/api/skills/aaron-he-zhu-send-experiment-designer/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "send-experiment-designer". Plan a subject-line test with a 40% baseline open rate and a 3-point target lift.
Expected outcome:
- Mode: A/B subject-line test
- Primary metric: open rate; guardrails: unsubscribes and spam complaints
- Plan: randomize recipients, keep content constant, set a fixed read date, and size each cell for the stated lift.
Using "send-experiment-designer". Interpret two variants with supplied delivered and conversion counts.
Expected outcome:
- Effect: reports the observed conversion-rate difference and uncertainty.
- Decision status: applies the named precommitted rule only when an owner and rule are supplied.
- Guardrails: identifies whether unsubscribe, complaint, or bounce thresholds were breached.
Security Audit
SafeMost static findings are false positives from Markdown formatting, documentation links, metadata URLs, and a statistical decimal. One documented command runs a local statistics helper; its numeric inputs and referenced script should be reviewed before use. No prompt-injection language, credential collection, data-exfiltration intent, or automatic network activity was found in SKILL.md.
Capability review items (1)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Risk Factors
βοΈ External commands (41)
π Network access (2)
π Filesystem access (14)
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APA citation
aaron-he-zhu. (2026). send-experiment-designer security audit report (audit version 8) [Author version 19.0.0]. Skillstore. https://skillstore.io/skills/aaron-he-zhu-send-experiment-designer/audits/8BibTeX citation
@techreport{aaron-he-zhu-aaron-he-zhu-send-experiment-designer-2026,
author = {aaron-he-zhu},
title = {send-experiment-designer security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/aaron-he-zhu-send-experiment-designer/audits/8},
note = {Author version 19.0.0}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "send-experiment-designer security audit report (audit version 8)"
version: "19.0.0"
type: report
authors:
- name: "aaron-he-zhu"
date-released: "2026-07-27"
url: "https://skillstore.io/skills/aaron-he-zhu-send-experiment-designer/audits/8"
identifiers:
- type: other
value: "skillstore:aaron-he-zhu-send-experiment-designer:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Plan a subject-line experiment
Define a controlled subject-line A/B test with sample size, guardrails, and a read date.
Assess a completed campaign test
Interpret delivered, open, click, and conversion counts with statistical and practical effect flags.
Measure incremental email value
Create a randomized hold-out plan to evaluate conversion or revenue lift from a send.
Try These Prompts
Design an email A/B subject-line test. My baseline open rate is 38%, my list has 12,000 people, and I want to detect a 3-point lift.
Create a send-time test for our weekly newsletter. Baseline open rate is 40%, the list has 20,000 people, and content must stay constant.
Plan a 2 by 2 subject and CTA experiment. Baseline click rate is 2.1%, send volume is 8,000 per week, alpha is .05, and power is .80.
Read this hold-out result using my decision rule. Control: 10,000 delivered and 180 conversions. Variant: 10,000 delivered and 215 conversions. Guardrail unsubscribe rates were 0.12% and 0.13%.
Best Practices
- Define one primary metric, a practical-effect threshold, and guardrails before sending.
- Keep one variable isolated per cell and preserve a randomized control group.
- Set the sample size and read date before the test, then avoid unplanned early decisions.
Avoid
- Changing subject, creative, and send time in one test cell.
- Calling a winner from an underpowered test or an early unplanned look.
- Treating statistical significance as an automatic business decision without an approved rule.
Frequently Asked Questions
What test types can this skill plan?
What data do I need for a new test?
What data do I need for a read-out?
Can it choose a winning campaign automatically?
Does it create email copy?
Does it require an email platform connection?
Developer Details
Author
aaron-he-zhuLicense
Apache-2.0
Author version
v19.0.0
Skillstore revision
r2
Ref
0715a6e09ea875c8e28cb705ce87cc83045e69c1
Maintenance freshness
7/28/2026
Usage
3 downloads Β· 1 views
File structure
π SKILL.md