--- 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 ```yaml 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 ```yaml 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 ```yaml chain_metadata: skill_slug: "ab-test-generator" stage: "analytics" timestamp: string suggested_next: - "performance-report" - "viral-post-writer" - "landing-page-creator" ``` --- Source attribution: Affitor Original project: https://github.com/Affitor/affiliate-skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: Affitor Original project: https://github.com/Affitor/affiliate-skills Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Marketing Roles: Advertising Specialist, Content Marketer, Email Marketer, Growth Marketer, Marketing Analyst Specialties: Cross Platform Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7cd824d11cd22012bce5bfede5cdab04f1df082b0acb9de97c44f447cc4cef43