Turn a winning TikTok or Meta ad into a structured creative test plan. Identify what made it work, plan distinct concepts, set test budgets and review timing, and use funnel metrics to decide whether to stop, iterate, or scale each ad.
Turn one winning ad into a creative testing plan for TikTok and Meta. Decodes why an ad won (hook, argument, buyer, format), plans concepts that Meta's Andromeda and TikTok treat as genuinely different (buyer × angle × format × first frame, not caption swaps), sets how many creatives to launch, the budget per test and when to judge, writes file and ad names that make results readable, reads hook rate, hold rate, CTR and CPA to find which part failed, and says when to kill, iterate or scale. Use when someone asks how many ads to test, how to make ad variants, why their variants all perform the same, whether an ad is fatigued, when to kill an ad, or how to scale a winner. NOT for producing the videos (use ecommerce-video-ads, ecommerce-video-edit or product-video-hooks) or for media-buying account setup.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Winners are rare. In Motion's 2026 benchmark (578,750 Meta creatives, $1.29B spend), only 5–8% of ads became winners (spend ≥10× the account median and ≥$500). One agency's 1,780 AI creatives produced 8 that crossed $100K in spend (self-reported). The plan exists to find the rare winner cheaply, then milk it.
Meta groups ads that look or feel alike as variations of one creative and says iteration "should not replace true creative diversification" (Meta). Agencies report that the same opening or the same look ranks as the same ad, and that 30 face and setting swaps of one winner made CPA triple. TikTok asks for 3–5 very different creatives per ad group and at least 12 new videos a month on Smart+ and GMV Max.
So a variant is a new concept: a different buyer, angle, format or first frame. A new caption, colour or voice on the same video is not a variant.
For the current winner (or the competitor ad that has run 30+ days), write down:
| Part | Question | Example |
|---|---|---|
| Hook | Pain callout, result shown, or visual interrupt? | Result shown: a clean oven at 0:00 |
| Argument | Why buy: speed, price, proof, transformation, comparison? | Speed: "60 seconds" |
| Buyer | Who actually bought (not the targeting)? | Renters, 25–35 |
| Format | Unboxing, demo, yapper, us vs them…? | Hands-on demo |
| Test | Keep | Change | If it wins |
|---|---|---|---|
| New hook | Argument, format, body | First 3 s (new frame, line, text) | The opener was the weak spot: make 5 more |
| New format | Argument | Demo → before/after → founder → podcast | Push the message into every format |
| New face / setting | Script | Creator, room (AI swaps OK here) | You extended the winner's life |
| New argument | Product | "Recommended by…", "30 days later…" | Start a new decode |
| New buyer | Product, format | Persona, age, room, problem | New audience pocket |
No winner yet (explore):
A winner exists (exploit): all-in on that angle. 3–5 new openers on its body, then 10 new executions of the concept + angle + persona. Operators report one of five new openers "will almost always outspend the others 30 to 1".
Cadence: about 2 concepts a week with 2 spoken-hook and 2 visual-hook versions each for brands under $100K/month (Savannah Sanchez); 15–21 high-intent ads a week at large scale (operator reports).
When to judge: not in the first 48 hours. Give each creative about 1–3× target CPA in spend (or ~5–10 conversions) before deciding. TikTok GMV Max: keep each ROI setting at least 3 full days.
| Symptom | Broken part | Fix |
|---|---|---|
| Low hook rate (3 s views ÷ impressions; Meta average 25.44% in H1 2026) | The first frame didn't earn the stop | New visual hook (product-video-hooks) |
| High hook rate, low hold (watched to 25%/50%) | Story tension lost after the hook | Tighter body, product earlier, cut dead air |
| High hold, low CTR | No commercial reason to click | Clearer offer, CTA said and shown |
| High CTR, low conversion | The landing page broke the promise | Fix the page, not the ad (one brand cut CAC from $140 to under $80 with the same ads by raising CVR 1% → 3%) |
| Views but no sales (organic) | Wrong audience or no buying intent | Judge by orders per 1,000 views; comments like "where do I buy?" are the signal |
Operator bands (not platform numbers): hook rate under 25% weak, 30–40% OK, 40%+ strong; hold under 15% weak, 35%+ strong; CTR under 1% weak, 3%+ strong. Always check CPA or ROAS before killing on hook rate alone.
{date}_{product}_{persona}_{angle}_{format}_{hook-id}_{creator}_{len}s_{cta}_v{N}
2026-10-27_serum_busy-mom_problem-fix_ugc-car_H07-confession_ava_28s_shopnow_v1
Keep a hook bank (every tested hook with its hook rate) and a concept log (persona, angle, format, result). Tag every ad by angle (PAIN / PROOF / CONTRAST / AUTHORITY…) so spend can be read per angle.
Deliver a table: concept, buyer, angle, format, first frame, hook line, skill that makes it, draft cost (from estimate_cost), budget, judge-after (CPA × N), file name. Then hand each row to the production skill.
ecommerce-video-ads · product-video-hooks · ecommerce-video-edit (one MP4 per hook) · make-video-ad-variants-for-testing on varg how to
Sources: Motion 2026 benchmarks, TikTok creative best practices, TikTok GMV Max, Jon Loomer on Creative Testing, Savannah Sanchez, Billo hook rates, operator posts on X (see ecommerce-video-ads/references/sources.md).
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.
Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.
Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.
Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.
Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.
Audit a project or learning workflow for scope drift, conflicting plans, inconsistent names, and code quality concerns. The skill reads project files and code, then reports evidence, severity, and suggested next steps without changing files.