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Ab Test Plan

skill-coleschaffer-copywritingskills-rmbc-ab-test-plan · by coleschaffer

Generate structured A/B test plans for DTC funnels — hypothesis, control vs variant, primary metric, sample size estimate, test duration, and success criteria using RMBC principles.

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Install

$ agentstack add skill-coleschaffer-copywritingskills-rmbc-ab-test-plan

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Security review

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No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

ab-test-plan

Purpose

Generate structured A/B test plans for DTC funnels. Testing is how you turn opinions into revenue data. Most teams waste tests by changing too many variables, running too short, or measuring the wrong metric. This skill produces a single, clean test plan with a falsifiable hypothesis, defined control and variant, primary metric, sample size estimate, expected duration, and success criteria. Every test plan connects back to RMBC — you're testing Research assumptions, Mechanism angles, Brief strategies, or Copy execution.

Inputs

| Input | Required | Description | |-------|----------|-------------| | page_type | Yes | What you're testing: landing_page, order_form, upsell, email, ad, checkout | | current_metric | Yes | Baseline performance: conversion rate, CTR, AOV, or revenue per visitor | | hypothesis | Yes | What you believe will improve performance and why | | traffic_volume | Yes | Daily unique visitors or impressions to the test page | | test_element | Yes | One of: headline, price, offer, layout, copy_length, mechanism, cta, guarantee, social_proof |

Execution Protocol

Step 1 — Load Framework Context

Read rmbc-context/SKILL.md to load RMBC framework definitions. A/B testing validates RMBC decisions with data — Research assumptions get tested through audience-facing copy, Mechanism strength gets tested through conversion lift, Brief strategy gets tested through engagement, Copy execution gets tested through click and purchase behavior.

Step 2 — Classify the Test Type

| Test Type | What Changes | Typical Lift | Risk Level | |-----------|-------------|-------------|------------| | Headline | Lead/hook copy only | 5-30% | Low — copy swap, no structural change | | Price | Price point or framing | 10-50% | Medium — affects AOV and refund rate | | Offer | What's included in the deal | 15-60% | Medium — may affect fulfillment | | Layout | Page structure, element order | 5-20% | Low-Medium — design change only | | Copy Length | Long vs short form | 10-40% | Low — same offer, different depth | | Mechanism | Which "why it works" angle | 10-35% | Low — copy change, different angle | | CTA | Button text, placement, urgency | 5-15% | Low — smallest change, quickest test | | Guarantee | Risk reversal type or duration | 5-25% | Low-Medium — may affect refund rate | | Social Proof | Testimonials, numbers, authority | 5-20% | Low — additive element |

Step 3 — Build the Test Plan

3a: Hypothesis Statement

Write a falsifiable hypothesis in this format: "Changing [element] from [control version] to [variant version] will increase [primary metric] by [estimated %] because [RMBC-grounded reason]."

The reason must connect to an RMBC phase:

  • Research-based: "Our ICP research shows the audience responds more to fear than desire"
  • Mechanism-based: "The current mechanism angle is too generic — the variant names a specific process"
  • Brief-based: "The page leads with features when proof should come first for this awareness level"
  • Copy-based: "The CTA is vague — benefit-driven button text reduces friction"
3b: Control vs Variant

Define exactly what stays the same and what changes. Only ONE variable changes per test.

3c: Primary Metric

Choose ONE primary metric. Secondary metrics are tracked but don't determine the winner.

| Page Type | Primary Metric | Secondary Metrics | |-----------|---------------|-------------------| | Landing page | Conversion rate (visitor → buyer) | Bounce rate, time on page, scroll depth | | Order form | Checkout completion rate | Cart abandonment rate, AOV | | Upsell | Take rate (% who accept) | Revenue per visitor, refund rate | | Email | Click-through rate | Open rate, unsubscribe rate, conversion | | Ad | CTR or CPA | CPM, frequency, relevance score |

3d: Sample Size Estimate

Calculate minimum sample size per variation using:

  • Baseline conversion rate (from current_metric)
  • Minimum detectable effect (MDE): 10-20% relative improvement (default 15%)
  • Statistical significance: 95% confidence (p Generated using RMBC framework by Stefan Georgi.

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.