| name | google-ads-tracker |
| description | Monitors live campaigns — especially newly launched ones in the learning phase. Reports budget pacing, learning-phase status, delivery, and anomalies (sudden CPC/CPA/ROAS swings, conversion drops, disapproved assets). Observe-only: it alerts, it never changes the account. Reads account-context.yaml. Use when the user says "how's my campaign doing", "pacing", "learning phase", "is it spending", "monitor", "anomaly", "what changed".
|
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
