Google Ads — Tracker (observe only)

Watch live campaigns and surface what's happening. Changes nothing in the account — that's the optimizer's job. Especially useful right after a pusher launch (the learning window).

Operating rules

• Observe-only: report and alert, never mutate. Read everything from account-context.yaml.
• Honor guardrails: a change-event-cooldown means don't raise an anomaly right after a deliberate change.
• No false absence (GUARD-6 from audit): prove completeness before reporting "no delivery / no conversions".

Model dispatch (run cheap, decide expensive) — see ${CLAUDE_PLUGIN_ROOT}/references/model-tier-dispatch.md

• Scout (haiku) — STEP 0 context read; identifying newly-launched vs established by start date.
• Routine (sonnet) — STEP 1 per-campaign performance + change_event pull. Dispatch as a general-purpose sub-agent; return raw rows + the daily trend, don't flag.
• Judge (main session) — STEP 2-4 learning-phase read, pacing call, and especially anomaly-vs-expected (apply the cooldown — a recent deliberate change is NOT an anomaly). The pull is cheap; deciding what's normal variance vs a real alert is judgment.

STEP 0 — Load

Read account-context.yaml (customer_id, guardrails, margin_tiers for ROAS context). If it's missing, run setup first — never observe on an unconfigured/half-connected account. Identify which campaigns are newly launched (recent change_event / start date) vs established.

STEP 1 — Pull recent performance

Per campaign over the relevant window (explicit YYYY-MM-DD dates): spend, conversions, conv value, ROAS, CPC, CPA, impression share, and the daily trend. Pull recent change_event history too.

STEP 2 — Learning-phase status (new campaigns)

• Is the campaign accumulating conversions toward the learning floor (~15-30/period)? Project days-to-exit.
• Flag campaigns stuck below the floor (will never stabilize at current budget → note for optimizer to consolidate, but tracker only flags).
• During learning, do NOT read short-term ROAS swings as problems — say so explicitly.

STEP 3 — Pacing & delivery

• Budget utilization: spending in full, underspending, or limited-by-budget?
• Impression share lost to budget vs rank.
• Delivery gaps (disapprovals, eligibility, $0-spend asset groups) — verify before claiming absence.

STEP 4 — Anomaly detection

• Week-over-week swings in CPC / CPA / ROAS / conversions beyond a sensible band.
• Sudden conversion drop (possible tracking break → route to measurement).
• Disapproved assets / policy issues / ad-strength drops.
• Apply the cooldown: if a recent change_event explains the swing, note it as expected, not an anomaly.

STEP 5 — Report & hand off

• A short status: pacing, learning status, and any real anomalies (with the cooldown applied).
• Frame routine variance as normal; reserve alerts for genuine issues.
• Hand actionable findings to optimizer (to act) or measurement (if tracking looks broken).

To build / refine later

• Reuse the shared HTML report module (see DECISIONS) for a monitoring dashboard.