Step of the analyze-performance playbook: calls ended tests, scores last week's calls, writes this week's kill/scale/hold calls and drafts learnings for the brand brain. Run analyze-performance instead; not for direct use.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Load: ../analyze-performance/references/tests-and-learnings.md.
Inputs: the brief's test plan; this week's results; last week's report (its calls); the change log (what was actually done); the learnings and readouts (brain_map kind 23 / brain_search where listed; else knowledge_search source_types: ["learning"], brand_read learnings); the paid run's newest run record.
Do:
../analyze-performance/references/run-memory.md): what was actually done (the ### YYYY-MM-DD — <title> blocks in decisions.md, run-paid-campaigns' run record), and what happened — for a money move, the paid run's verdict plus the store-joined check over the same window; right or wrong.experiment_readout.<campaign-slug>-t<n> through save-learning's readout path, only with the marketer present; scheduled runs mark it readout to save.save-learning skill (../save-learning/SKILL.md) — one idea per learning, [learning YYYY-MM-DD] (+ A-NN), their exact words as the statement, own superseded facts retired in the same call; read back, compare, report which landed. Without their words, write nothing.Output: the report's Tests, Calls, Learnings-proposed sections; the learnings rows written after the marketer has seen them.
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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