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OpenAI Ads playbook — campaign structure, appearances in AI answers, and aggregation levels. Use when planning, reading, auditing, or launching OpenAI Ads, or porting a campaign to OpenAI Ads.
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
Expand targeting without wrecking CPA — lookalike ladders, seed quality, audience layering, and exclusion hygiene across platforms. Use when a user wants to scale beyond exhausted audiences, build lookalikes, fix audience fatigue, or set up exclusion lists.
Build a post-cookie first-party data strategy — Enhanced Conversions, server-side tracking, Consent Mode v2, and activating owned data for targeting. Use when a user wants to recover lost conversion data, set up Enhanced Conversions or CAPI, implement consent mode, or plan for cookie deprecation.
Verify that ad campaigns actually target the geographies they claim — location criteria exist, presence-only matching is set, and click geography matches intent. Use when auditing campaigns, investigating cheap clicks or missing conversions, or reviewing any campaign before and after launch.
Diagnose and improve Google Ads Quality Score — Expected CTR, Ad Relevance, and Landing Page Experience — to cut CPCs and lift Ad Rank. Use when CPCs look high for the vertical, impression share is capped by rank, or keywords sit at QS 5 and below.
Optimize Google Shopping — product feed quality, titles, Merchant Center disapprovals, supplemental feeds, custom-label segmentation, and Shopping vs Performance Max structure. Use when Shopping impression share is low, products are disapproved, or feed titles and labels need work.
Design and run incrementality tests that measure true lift from ads — geo holdouts, platform lift studies, significance math, and iCPA decisions. Use when a user wants to know whether ads actually cause conversions, design a holdout test, or decide budget based on real lift.
| name | platform-openai |
| description | OpenAI Ads playbook — campaign structure, appearances in AI answers, and aggregation levels. Use when planning, reading, auditing, or launching OpenAI Ads, or porting a campaign to OpenAI Ads. |
A newer surface: ads that appear alongside AI answers. Structure is campaign → ad group → ad. Treat it as an emerging channel, measure honestly, and start small.
list_connected_accounts, then list_campaigns(platform="openai_ads").pull_openai_ads_performance(days=7, aggregation_level="campaign") for metrics. Scope tighter with ad_group_id or ad_id, or set aggregation_level to ad_group or ad.generate_text_ad / generate_image_ad and apply brand voice from ${CLAUDE_PLUGIN_ROOT}/context/brand-and-safety.md.create_campaign_plan + upsert_plan_entity; forecast_campaign where projections are available. Run campaign-preflight: tracking attached (verify_pixel_ownership / ga4_list_conversions), sane budgets.execute_campaign_plan, then enable_campaign. Nothing that spends money goes live without a clear go.list_campaigns(platform="openai_ads") and report the real ID. Start conservative; read results before scaling with the optimize skill.Porting from another platform? Use the replicate skill and translate keyword intent into the questions this audience asks.