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Ab test generator

Skill Affitor/affiliate-skills/skills/analytics/ab-test-generator

50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social intelligence. Works with Claude Code, Pi, ChatGPT, Gemini, Cursor, Windsurf, any AI.

Install
npx -y skills add Affitor/affiliate-skills --skill ab-test-generator

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What its author says it does

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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".

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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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 context. Paste the full headline, CTA, or email subject line you want to test."
  • Content type unclear: "Is this a headline, CTA button text, email subject line, or social post hook? Knowing the format helps me generate better variants."
  • Too many variants requested (>5): "I'll generate 5 high-quality variants. More than 5 makes testing impractical — you'd need a very large audience to reach statistical significance."

Examples

Example 1: Blog headline test

User: "Test this headline: 'HeyGen Review: Is It Worth It in 2026?'" Action: Generate 3 variants. Variant A: "I Tested HeyGen for 30 Days — Here's What Happened" (curiosity + personal experience). Variant B: "HeyGen vs Synthesia: Which AI Video Tool Wins?" (comparison + specificity). Variant C: "The AI Video Tool That Cut My Production Time by 80%" (result + specificity). Each with hypothesis.

Example 2: CTA button test

User: "Optimize this CTA: 'Start Free Trial'" Action: Variant A: "Try HeyGen Free — No Card Required" (reduces friction). Variant B: "Create Your First AI Video in 2 Minutes" (outcome-focused). Variant C: "Get Started Free →" (shorter, action-oriented). Test plan: minimum 500 clicks per variant, track conversion rate.

Example 3: Email subject line test

User: "I'm sending an email about Semrush. Test this subject: 'Check out Semrush — it's great for SEO'" Action: Identify weakness (vague, no hook). Variant A: "The SEO tool I use to rank #1 (not kidding)" (social proof + curiosity). Variant B: "Your competitors are using this — are you?" (FOMO). Variant C: "3 Semrush features that doubled my organic traffic" (specificity + result). Each preserves FTC compliance.

References

  • shared/references/ftc-compliance.md — Ensure variants preserve FTC disclosure from original. Referenced in Step 3.
  • shared/references/flywheel-connections.md — master flywheel connection map

Flywheel Connections

Feeds Into

  • purple-cow-audit (S1) — winning variants reveal what resonates = what's remarkable
  • performance-report (S6) — test results for reporting

Fed By

  • viral-post-writer (S2) — posts to test variations of
  • twitter-thread-writer (S2) — thread hooks to test
  • landing-page-creator (S4) — landing page elements to test
  • content-pillar-atomizer (S2) — volume mode variants for testing

Feedback Loop

  • Test results directly improve all content-producing skills → winning headlines, CTAs, and angles feed into next content creation cycle
chain_metadata:
  skill_slug: "ab-test-generator"
  stage: "analytics"
  timestamp: string
  suggested_next:
    - "performance-report"
    - "viral-post-writer"
    - "landing-page-creator"

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