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/ab-test-generator

Generate A/B test variants for affiliate content. Triggers on: "create A/B test", "test my headline", "optimize my CTA", "generate variants", "split test ideas", "improve click-through rate", "test my landing page copy", "headline alternatives", "CTA variations", "which version

From plugin
affiliate-skills
59652 skills3 commands
Install
$ npx -y skills add Affitor/affiliate-skills --skill ab-test-generator --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ab-test-generator

Context preview

The summary Claude sees to decide when to auto-load this skill.

Generate A/B test variants for affiliate content. Triggers on: "create A/B test", "test my headline", "optimize my CTA", "generate variants", "split test ideas", "improve click-through rate", "test my landing page copy", "headline alternatives", "CTA variations", "which version

SKILL.md

ab-test-generator.SKILL.md
name: ab-test-generator
description: >
  Generate A/B test variants for affiliate content. Triggers on:
  "create A/B test", "test my headline", "optimize my CTA", "generate variants",
  "split test ideas", "improve click-through rate", "test my landing page copy",
  "headline alternatives", "CTA variations", "which version is better",
  "optimize conversions", "test my email subject line", "compare approaches".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "analytics", "optimization", "tracking", "ab-testing", "experiments"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S6-Analytics

A/B Test Generator

Generate A/B test variants for affiliate content — headlines, CTAs, landing page sections, email subject lines, and social post hooks. Each variant includes a hypothesis explaining why it might outperform the original. Output is a Markdown document with the original, variants, hypotheses, and a test plan.

Stage

S6: Analytics — Small changes in headlines and CTAs can swing conversion rates by 20-50%. A/B testing is how professional affiliates systematically find what converts best. This skill removes the guesswork by generating theory-driven variants using proven copywriting frameworks.

When to Use

  • User wants to improve conversion rates on existing content
  • User has a headline, CTA, or email subject line and wants alternatives
  • User says "test my headline", "optimize my CTA", "A/B test ideas"
  • User has a landing page section that isn't converting
  • User wants to compare different messaging approaches
  • Chaining from S2-S5: take any content output and generate test variants

Input Schema

original: string               # REQUIRED — the content to test (headline, CTA, paragraph,
                               # email subject line, or full social post)

content_type: string           # REQUIRED — "headline" | "cta" | "landing_section"
                               # | "email_subject" | "social_hook"

goal: string                   # OPTIONAL — "clicks" | "signups" | "purchases"
                               # Default: "clicks"

num_variants: number           # OPTIONAL — number of variants to generate (2-5)
                               # Default: 3

audience: string               # OPTIONAL — who sees this content
                               # (e.g., "SaaS founders", "content creators")

product: string                # OPTIONAL — product being promoted

**Chaining context**: If S2-S5 content exists in conversation, the user can reference it: "test the headline from my blog post" or "generate CTA variants for my landing page."

Workflow

Step 1: Analyze Original Content

Break down the original into components:

  • **Emotional angle**: What emotion does it trigger? (curiosity, fear, desire, urgency)
  • **Specificity**: How specific vs vague?
  • **Structure**: Question, statement, command, statistic?
  • **Framework**: Which copywriting framework does it follow? (PAS, AIDA, 4U, BAB)

Step 2: Identify Testable Elements

Determine what to vary:

  • Emotional angle (switch from curiosity to urgency)
  • Specificity (add numbers, remove vagueness)
  • Structure (question vs statement)
  • Length (shorter vs longer)
  • Power words (swap key words for stronger alternatives)
  • Social proof (add or remove)

Step 3: Generate Variants

Create `num_variants` alternatives, each using a different approach:

  • **Variant A**: Different emotional angle
  • **Variant B**: Different structure/format
  • **Variant C**: Different specificity level
  • Additional variants explore social proof, urgency, or contrarian angles

Each variant must:

  • Preserve the core message and product reference
  • Preserve any FTC disclosure from the original
  • Be a realistic alternative (not just a word swap)

Step 4: Write Hypotheses

For each variant, explain:

  • What was changed and why
  • Which copywriting principle supports the change
  • What behavior change is expected (e.g., "Higher CTR because questions create open loops")

Step 5: Suggest Test Plan

Recommend:

  • Sample size needed (minimum 100 impressions per variant for social, 500 for landing pages)
  • Test duration (7-14 days minimum)
  • What metric to track (CTR, conversion rate, revenue per visitor)
  • When to declare a winner (95% statistical significance or practical significance threshold)

Step 6: Self-Validation

Before presenting output, verify:

  • [ ] 3-5 distinct variants generated (not just word swaps)
  • [ ] Each hypothesis grounded in a copywriting principle or framework
  • [ ] Sample size calculation is present and realistic
  • [ ] Test duration is ≥7 days minimum
  • [ ] Winner criteria defined with statistical significance threshold

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
test:
  original: string
  content_type: string
  goal: string

variants:
  - label: string              # "Variant A", "Variant B", etc.
    content: string            # the variant text
    change: string             # what was changed
    framework: string          # copywriting principle used
    hypothesis: string         # why this might win

test_plan:
  sample_size: number          # per variant
  duration: string             # recommended test period
  metric: string               # what to measure
  winner_criteria: string      # when to pick a winner

Output Format

1. **Original** — the current content being tested 2. **Variants** — each variant with its content, change description, and hypothesis 3. **Test Plan** — sample size, duration, metric, winner criteria 4. **Quick Win** — if one variant is clearly stronger based on copywriting principles, call it out

Error Handling

  • **Original too short (1-2 words)**: "I need more cont
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