Overview
What it does
Turns a winning ad into 8 disciplined A/B variants that each change exactly one element, with a hypothesis and a test plan.
When to use it
You have a winning ad and want systematic variants to A/B test — not random rewrites. Each variant should change ONE variable so you can actually learn what moves the needle.
Pairs with MCP
The MCP lets the model pull the original ad's exact copy, format, length, and performance — then generate variants that respect those constraints.
Best for
Copywriters, performance marketers, growth teams.
Install
Drop this entire block into a SKILL.md file inside your Claude project’s .claude/skills/ad-variant-writer/ folder. Claude auto-invokes it whenever you have a winner and want statistically meaningful A/B variants.
---
name: ad-variant-writer
description: Use this skill whenever the user has a winning ad and wants statistically meaningful A/B variants — not random rewrites. Generates 8 variants that each test one variable while holding everything else constant.
---
# Ad Variant Writer
You write systematic A/B variants. Each variant changes exactly one element so the user can isolate what moves conversions.
## Data you need
- The original ad copy (primary text, headline, description, CTA)
- The format and length
- Why it's the winner (ROAS, CTR, or both — pulled from Uplifted)
## The 8 variants (always produce all 8)
For each variant, change only the listed element. Keep everything else identical to the original.
1. Hook swap — new opening line, same body
2. Body length cut — same hook + CTA, body trimmed by ≥30%
3. Social proof injection — add one specific number/testimonial; remove no other element
4. Benefit reframe — same feature, new emotional benefit
5. CTA swap — different CTA verb (e.g., "Shop now" → "See the lookbook")
6. Specificity — add one precise number, time, or proof point
7. Identity tweak — open with "For [audience]" framing
8. Urgency layer — add a soft urgency cue without changing the offer
## Output format
For each variant:
Variant N — [What's being tested]
Hypothesis: [what we expect to happen and why]
Primary text: [...]
Headline: [...]
Description: [...]
CTA: [...]
End with a Test plan section:
- Recommended split: equal budget across all 8 + control = 9 cells
- Minimum sample size per cell: 1,000 impressions before reading results
- Primary metric: [CTR / CVR / ROAS depending on objective]
- Stop conditions: when to kill underperformers
## Guidelines
- Only one element changes per variant. If you change two, you're testing nothing.
- Every variant should be at least as long as the original. Shorter often wins, but you can cover that explicitly with variant 2.
- The "Hypothesis" is mandatory — without it the test produces a winner with no learning.Prompt
Takes one winning ad and writes 8 disciplined A/B variants that each change exactly one element, each with a hypothesis and a test plan. Use it to extend a winner without muddying what you're actually testing.
You write systematic A/B variants. I have a winning ad. Generate 8 variants that each change EXACTLY one element so I can isolate what's moving conversions.
Original ad:
- Primary text: {{paste}}
- Headline: {{paste}}
- Description: {{paste}}
- CTA: {{paste}}
- Format & length: {{specify}}
- Why it's winning: {{ROAS / CTR from Uplifted}}
Produce 8 variants. Each changes only one of:
1. Hook swap (new opening)
2. Body length cut (same hook+CTA, trim body 30%+)
3. Social proof injection
4. Benefit reframe (same feature, new emotional benefit)
5. CTA swap (different verb)
6. Specificity (precise number/time/proof)
7. Identity tweak (open with "For [audience]")
8. Urgency layer (soft urgency cue)
For each variant return: Variant N — [what's being tested] | Hypothesis | Primary text | Headline | Description | CTA.
End with a Test plan: equal-split budget across 8 + control, min 1,000 imp/cell before reading, primary metric, and stop conditions.
Hard rule: only ONE element changes per variant. Hypothesis is mandatory.
EXPECTED OUTPUT:
- 8 variants ready to drop into Ads Manager
- A hypothesis per variant (the actual reason to test it)
- A statistically sound test plan