Test calls, calls and learnings (atom)

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:

1. Call each ended test Win / Loss / Unclear against its written criteria with the brief's action; state how sure (confirmed / likely / first look).
2. Score last week's recommendations through run-memory's outcome check (../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.
3. Write this week's calls (kill / scale / hold, Google negatives) with object, why, how sure and "would change if"; first look → "watch".
4. Check fired replan triggers against the brief's thresholds.
5. Draft ≤ 3 learnings as one-line rules (template in the reference); only ended tests, 2+ week patterns that clear the test n floors on the referee-joined number, confirmed surprises or failures; each shows what it would retire.
6. Readouts: each Win / Loss → kind 23 experiment_readout.<campaign-slug>-t<n> through save-learning's readout path, only with the marketer present; scheduled runs mark it readout to save.
7. After the marketer's reply to the decisions block: write the kept learnings through the 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one idea per learning, [learning YYYY-MM-DD] (+ A-NN), their exact words as the statement, own superseded facts retired in the same call; read back, compare, report which landed.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.