複数の独立したローカル作業を安全性と依存関係を確認して実行計画にまとめ、対応環境では並列化やAppleシリコン向け処理の選択を支援します。
- ビルドやテストの独立した処理を並列化したいとき
- 複数段階の作業計画を安全に実行したいとき
- Appleシリコン向け処理の候補を確認したいとき
TikTokやMeta広告の勝ちパターンを分解し、異なる切り口のテスト案や予算、評価時期を整理して、数値から継続・修正・停止を判断するスキルです。
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.
実行スコア 未検証
インストール方法を見る成果の出た広告を、冒頭の見せ方、購入理由、購入者像、動画形式に分けて分析し、次に試す広告案を組み立てます。字幕や色だけを変えた案ではなく、購入者像・切り口・形式・最初の画面を変えた案を設計し、各案の予算や評価するタイミング、ファイル名・広告名も整理します。広告を見た人の割合、視聴維持率、クリック率、獲得単価などから、どこで反応が落ちたかを読み取り、停止・修正・拡大の目安を示します。
新しい広告を何本試すか決めたいときや、似た案ばかりになって成果が変わらないときに役立ちます。広告の反応が落ちた原因を見極めたい場合や、勝ち広告をもとに次のテストを考える場合にも使えます。動画の制作や広告アカウントの設定ではなく、テスト計画と結果の読み取りを担います。
計画作成に外部ツールは不要です。広告指標を使った分析には、共有されたCSV形式のデータを読み込みます。動画制作や広告アカウントの設定は対象外です。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
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複数の独立したローカル作業を安全性と依存関係を確認して実行計画にまとめ、対応環境では並列化やAppleシリコン向け処理の選択を支援します。
将来の読み手が理由を知りたくなる未決定や暫定対応を、技術的負債として登録し、後から項目ごとに整理できるスキルです。
LangChain、EINO、AutoGen、AgentScope、CrewAI向けのコード雛形やAPI対応、プロジェクト構成を参照し、バージョン確認を踏まえてAIエージェント開発を支援します。
エラーやテスト失敗、画面の異常などを再現して原因を特定し、テストで確認しながら影響を絞った修正を進めるデバッグ用スキルです。
対話を通じてAI Agentの要件整理から設計、ツール定義、コード生成、動作確認まで6段階で進め、実行可能なプロジェクトを作ります。
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