Evaluate completed tests against their stated criteria, check how last week’s recommendations performed, and draft this week’s stop, scale, or hold decisions. It also proposes concise brand learnings from supported evidence; run it through the main performance analysis playbook.
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.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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 (brand_knowledge_browse kind 23 / brand_knowledge_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— one idea per learning, [learning YYYY-MM-DD] (+ A-NN), their exact words as the statement, own superseded facts retired in the same call; read back, compare, report which landed.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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The supplied material contains only placeholder text for the skill’s purpose, inputs, steps, and completion criteria. It does not provide enough information to describe a task or result reliably.
Research supported advertising angles using buyer context, current product facts, customer language, and observed ads. Turn the evidence into static ad, video, or copy ideas, matching video concepts to available recipes. Scores rank ideas for testing; they do not predict results.
Analyze campaign data from Google, Meta, and LinkedIn to find strong and wasteful spend. The skill checks funnel performance and test results, then recommends which campaigns to pause, scale, or test and how to shift budget across channels.
Compare ad spend and signups with downstream outcomes to calculate the cost of each qualified lead. The skill checks tracking coverage, accounts for how long leads need to mature, and groups creatives into actionable categories with confidence notes.
Prepare the intake for a Meta ad campaign by reviewing available brand information first, stating reasonable assumptions, and asking only questions whose answers could affect budget, claims, or legal exposure. It records stable brand facts, a campaign brief, and what was asked versus assumed.
Review ad copy, image text, visuals, and people against platform and brand category rules. It suggests honest fixes and needed disclosures, then flags issues requiring human or legal review, with a separate result for each ad and platform.