experiment-result-readerlisted

# Experiment result reader A/B tests die from two failure modes that have nothing to do with the variant: reading the result before the sample is big enough, and reading the result without controlling for the wrong thing. This skill encodes the discipline analysts use to avoid both. ## When NOT to run this - The user wants to *design* a new experiment (sample-size estimation, MDE, variant logic). That's a different skill: design vs read. - The experiment isn't declared in `event-schema.yaml` and exposure events aren't being fired. There's nothing to read; nudge the user toward instrumenting the experiment first via `event-schema-author` or their platform of choice. - The conversion metric is more than three steps removed from exposure (e.g. "exposure → 30-day retention → upgrade → renewal"). Sequential / cohort-windowed analysis is genuinely harder; this skill stays at the per-period rate level and is honest about it. ## Method ### Phase 1. Verify the setup Before reading anything, confirm two facts: 1. **The experiment is declared.** Read `event-schema.yaml`'s `experiments:` section. The experiment should have a name, variants list, and ideally an `intent`. If it isn't declared, ask the user where the variant assignments live and whether the exposure event is firing reliably. 2. **Exposure events are landing for every variant.** Query the analytics platform (with Clamp: `events.list(name="$exposure", group_by="variant")` or whichever event name is canonical for the pl