lifesight-anomaly-watchlisted

# Lifesight Anomaly Watch Tell the user what actually moved and what to do about it — separating real, actionable shifts from noise. **The value is the triage, not the list.** A dump of every wobble is worse than useless; the job is to say "act on these two, ignore the rest, here's why." **Prerequisites (router handles):** workspace calibrated, profile loaded. Operate under `lifesight-core`; present under `lifesight-rendering`. Load both. ## The core judgment: act vs noise Every flagged movement gets a tag. A movement is **act**-worthy only if it is BOTH: - **Statistically real** — beyond normal variance (e.g. >2σ), not a one-day blip. - **Causally material** — it reflects a real efficiency/outcome shift, not a platform-reporting artifact, a known seasonal pattern, or a change the user already made (a campaign launch, a budget change). If it's explainable, it's context, not an alarm. Everything else is **noise** — name it as noise so the user can stop worrying about it. ## Flow 1. **Scope the window and surface.** Default to the period the user names ("last week") or the most recent complete week. Don't scan "everything, all time". 2. **One heavy call.** Pull the movement analysis via `ask_mia` (it summarizes). If you need a specific metric series, a single scoped `query_ad_data` is fine — never a broad dump (`lifesight-core` Rule 3). 3. **Classify and rank.** Tag each anomaly act/noise; rank the act items by causal materiality (impact on incremental re