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Skills ab-test-setup
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ab-test-setup
Content revision r2 Safe
Design Rigorous A/B Tests
Weak experiment design creates misleading results and wasted traffic. This skill applies hypothesis, metrics, sample size, tracking, execution, and analysis gates before decisions.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "ab-test-setup" from https://skillstore.io/skills/sickn33-ab-test-setup.md and its manifest at https://skillstore.io/api/skills/sickn33-ab-test-setup/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 "ab-test-setup". We want to test a shorter checkout for mobile visitors.
Expected outcome:
- Proposed hypothesis: A shorter checkout will increase completed purchases among mobile visitors.
- Primary metric: completed purchases per assigned mobile visitor.
- Required inputs: baseline conversion, target MDE, traffic volume, guardrails, and final variant definition.
Using "ab-test-setup". Baseline conversion is 8%, MDE is 10% relative, power is 80%, and significance is 95%.
Expected outcome:
- The planning inputs are defined, but daily eligible traffic and assignment unit are still required.
- Confirm whether the MDE means an increase from 8% to 8.8%.
- Calculate duration only after traffic stability and user independence are assessed.
Using "ab-test-setup". Review our launch plan. Events fire, but variant IDs appear only on entry events.
Expected outcome:
- Launch status: blocked.
- Variant attribution must appear on every event used by primary and secondary metrics.
- Repair attribution, test at least five events per variant, and repeat the readiness review.
Security Audit
Safev5 • 7/23/2026 Open versioned report
All five static findings are false positives caused by ordinary experiment-design language in SKILL.md. The skill contains procedural A/B testing guidance and no reconnaissance, prompt injection, command execution, or data-exfiltration intent.
1
Files scanned
258
Lines analyzed
0
Review items
0
False positives ignored
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
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<iframe src="https://skillstore.io/embed/skills/sickn33-ab-test-setup.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). ab-test-setup security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-ab-test-setup/audits/5BibTeX citation
@techreport{sickn33-sickn33-ab-test-setup-2026,
author = {sickn33},
title = {ab-test-setup security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-ab-test-setup/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ab-test-setup security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-ab-test-setup/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-ab-test-setup:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
sickn33-ab-test-setup
Skillstore Score 78
Evidence Confidence High
Skillstore usage 9
Updated
2026-08-21
coreyhaines31-ab-test-setup
Skillstore Score 77
Evidence Confidence High
Skillstore usage 7
Updated
2026-08-21
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Validate a Product Experiment
Turn a product idea into a locked hypothesis, measurable outcome, guardrails, and launch checklist.
Plan Reliable Measurement
Define metrics, estimate sample needs, and verify event collection before exposing users to variants.
Review Experiment Readiness
Identify missing assumptions, tracking risks, and stopping rules before implementation or launch approval.
Try These Prompts
Draft a Hypothesis
Help me turn this idea into a valid A/B test hypothesis: [idea]. Ask for the audience, metric, direction, baseline, and MDE.
Review Hypothesis Quality
Review this hypothesis and stop if required details are missing: [hypothesis]. Identify assumptions, one primary metric, secondary metrics, and guardrails.
Plan Sample and Duration
Plan an experiment using baseline [rate], MDE [effect], significance [level], power [target], and daily traffic [volume]. State assumptions and estimate sample size and duration.
Run a Readiness Review
Audit this complete experiment plan: [plan]. Check design choice, metrics, sample assumptions, tracking, randomization, guardrails, stopping rules, analysis, and documentation. Block unresolved requirements.
Best Practices
- Lock the hypothesis, audience, primary metric, expected direction, and MDE before designing variants.
- Verify tracking and randomization with raw records before launch, then monitor technical health without peeking at outcomes.
- Document assumptions, external factors, achieved sample, results, guardrails, decisions, and follow-up learning.
Avoid
- Do not change variants, traffic sources, success criteria, or the primary metric after launch.
- Do not stop early because interim results appear favorable or unfavorable.
- Do not ship a primary-metric win when a predefined guardrail fails.
Frequently Asked Questions
What information should I provide first?
Provide the user problem, proposed change, target audience, primary metric, baseline, MDE, traffic estimate, and known external factors.
Can this skill choose the test type?
Yes. It compares A/B, A/B/n, multivariate, and split URL designs, while preferring the simplest valid option.
Does this skill calculate sample size?
It can guide an estimate when baseline, MDE, significance, power, assignment unit, and traffic are available.
Can I launch with incomplete tracking?
No. Event delivery, variant attribution, duplicate prevention, randomization balance, and guardrail monitoring must be verified first.
How does it prevent biased decisions?
It freezes success criteria, discourages interim peeking, preserves guardrails, and separates statistical significance from business judgment.
What happens when required inputs are missing?
The workflow stops, explains the missing requirement, and recommends the evidence or redesign needed to continue.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
ebdfe608f5de2b66ff37ab4af12af8ac4f5e8006
Maintenance freshness
7/26/2026
Usage
8 downloads · 133 views
File structure
📄 SKILL.md
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