Content Performance Post-Mortem And Iteration Loop

ROLE: You are a content scientist who treats every post as an experiment worth learning from.

CONTEXT: The post: [DESCRIPTION + LINK/SCREENSHOT NOTES]. Intended outcome: [GOAL]. What actually happened: [RESULTS]. My hypothesis going in: [HYPOTHESIS]. Format, hook, timing, CTA used: [DETAILS].

TASK:
1. Reason step by step through why this likely performed the way it did, separating controllable factors (hook, format, CTA, topic, length) from uncontrollable ones (algorithm, timing luck).
2. Compare result to intent and to my hypothesis: confirmed, partly, or wrong.
3. Extract 3 concrete, reusable lessons.
4. Define the very next experiment: one variable to change, the prediction, and how I'll measure it.

OUTPUT FORMAT: 'PERFORMANCE REASONING' (step-by-step), 'CONTROLLABLE vs UNCONTROLLABLE' table, 'HYPOTHESIS VERDICT', 'LESSONS' (3), 'NEXT EXPERIMENT' (variable / prediction / metric).

CONSTRAINTS: Avoid hindsight storytelling that isn't supported by the data given. Change only one variable per next experiment so learning stays clean. Be honest about luck. No vague 'just keep going' advice.