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A/B testing strategy and implementation for funnel pages. Defines what to test, how to structure variants, statistical significance thresholds, and common testing patterns.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Decision-tree skill that recommends the optimal funnel type based on the user's business model, price point, audience, and goals. Returns a ranked recommendation with reasoning.
Builds a 2-page community group growth funnel. Drives free opt-ins to a group (Skool, Whop, Facebook Group, Discord, or any community platform) using a lead magnet. Use when the goal is to grow a free or paid community.
Reverse-engineer any live public sales funnel and rebuild it — adapted for the user's brand, copy, and offer — without copyright infringement. Handles single pages or entire multi-page funnels. Uses Russell Brunson's Hook/Story/Offer framework to deconstruct what makes a funnel convert, then delegates the build to the funnel-builder agent.
Automated evergreen webinar funnel. Simulates a "live" experience on-demand so leads can register and watch immediately (or at scheduled slots). Best for scaling a proven webinar without live hosting.
High-ticket sales funnel for offers $3,000+. Combines authority content, social proof, and a booked-call CTA. Best for coaching programs, masterminds, done-for-you services, and enterprise solutions.
Recurring membership/subscription funnel. Acquires members with a free trial or low entry, then retains with ongoing value. Best for communities, content libraries, and recurring SaaS.
| name | ab-testing |
| description | A/B testing strategy and implementation for funnel pages. Defines what to test, how to structure variants, statistical significance thresholds, and common testing patterns. |
Test everything. Opinions are nice — data is better.
| Priority | Element | Expected Impact |
|---|---|---|
| 🔴 P0 | Headline | 10–50% lift |
| 🔴 P0 | CTA text + color | 5–30% lift |
| 🟡 P1 | Hero image/video | 5–20% lift |
| 🟡 P1 | Form fields (fewer vs. more) | 10–40% lift |
| 🟡 P1 | Social proof placement | 5–15% lift |
| 🟢 P2 | Page layout (long vs. short) | 5–20% lift |
| 🟢 P2 | Pricing display | 5–25% lift |
| 🟢 P2 | Urgency messaging | 3–15% lift |
| 🔵 P3 | Color scheme | 2–10% lift |
| 🔵 P3 | Font choices | 1–5% lift |
HYPOTHESIS: If we change [element] from [current] to [proposed],
then [metric] will [increase/decrease] by [estimated %]
because [reasoning based on conversion principles].
TEST SETUP:
- Control (A): [Current version description]
- Variant (B): [New version description]
- Primary metric: [Conversion rate / Click rate / etc.]
- Secondary metric: [Revenue / Engagement / etc.]
- Required sample: [Number] visitors per variant
- Estimated duration: [X] days at [Y] daily visitors
After each test, log:
TEST: [Test Name]
DATE: [Start] → [End]
TRAFFIC: [Total visitors] ([Per variant])
RESULTS:
Control: [X]% conversion ([N] conversions)
Variant: [Y]% conversion ([N] conversions)
WINNER: [Control/Variant]
LIFT: [+/- X]%
CONFIDENCE: [X]%
NEXT: [What to test next based on learnings]