memory/ad/budget-pacing-monitor/

• Reads: the campaign/flight under watch, its budget (daily or lifetime) and flight window, the intended target curve (even / front-loaded / back-loaded), the live campaign report export (spend by day, impression share lost to budget if present, delivery status), and the learning-phase status per platform.
• Writes: a user-facing pacing table plus a reusable pacing summary storable under

  memory/ad/budget-pacing-monitor/

  .
• Promotes: a fired reallocation trigger, the projected end-of-flight spend, and the next pacing-check date to

  memory/open-loops.md

  ; ask before writing.
• Done when: spend is read against a target curve fixed before the check (not a bare "spent X of Y"); learning-phase status is confirmed before any over/under-delivery call is acted on; the verdict is one of the four with its percent-to-pace; and the reallocation trigger is fire/hold with the band it crossed named.
• Primary next skill: use the

  Next Best Skill

  below.

Emit the standard shape from skill-contract.md §Handoff Summary Format.

• ~~ad platform

  (own data) — campaign report CSV exported from the native ad manager: spend by day, budget (daily/lifetime), delivery/serving status, and impression share lost to budget where the platform reports it (the direct over-delivery signal).

• ~~web analytics

  (GA4) — Traffic-acquisition export, optional, only to sanity-check that pacing changes track a real conversion pattern rather than a delivery artifact.

1. Fix the target curve first. Record the budget (daily or lifetime), the flight window (start/end), and the intended pace: even (spend/day flat), front-loaded (heavier early), or back-loaded (heavier late). Default to even only if the user has no stated shape. The target curve is the yardstick — set it before reading spend, not after, so the read is pace-vs-plan and not a bare percentage.
2. Confirm learning-phase status before acting. If the campaign is still in learning phase, say so and do not fire a reallocation trigger — moving budget or editing in learning resets it and the pace signal is noise. Note the learning-exit date; a pacing read inside learning is observational only. Premature scaling / learning-phase violation is a high-severity S guardrail, not a veto — flag it, do not score it (that is the auditor's job).
3. Snapshot spend to the ledger. Record cumulative spend and elapsed-flight so the delta is computed, not eyeballed:

   python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/ledger.py" record <campaign> --source paid --data '{"spend": ..., "budget": ..., "days_elapsed": ..., "days_total": ...}'

   , then

   ledger.py trend <campaign> --source paid --field spend

   for the spend line across prior checks.
4. Compute percent-to-pace. Compare cumulative spend against where the target curve says it should be at this point in the flight:

   pace = actual_cumulative_spend / expected_cumulative_spend_at_this_point

   . State it as a percent (e.g. "at 138% of pace — spend is running ahead of the curve"). For lifetime budgets, project end-of-flight spend at the current rate and compare to the cap.
5. Call over- or under-delivery and name the driver. Over-delivery: pace > band and impression-share-lost-to-budget is high or the daily cap is exhausted early — spend is outrunning the plan. Under-delivery: pace < band with budget left on the table — usually bid-throttled, low search volume, narrow audience, or dayparting. Name the likely driver from the export; separate the observed pace gap from its plausible cause.
6. Decide the verdict and the reallocation trigger. Verdict: On-track (pace inside the band), Ahead (over-delivering past the band), Behind (under-delivering past the band), Stalled (near-zero recent spend / not serving). Then the trigger — fire a reallocation when the gap crosses the stated band and learning has exited (route the actual move to

   budget-optimizer

   ), or hold when inside the band or still in learning. Record: campaign · budget · flight window · target curve · percent-to-pace · verdict · driver · trigger (fire/hold) · band · next-check date.

budget-optimizer

bid-strategy-planner

ad-account-auditor

• ROAS Benchmark — the S (Spend-efficiency) dimension: budget pacing & allocation and the learning-phase-respect guardrail this skill watches; note that premature scaling is a flag under S, not a veto.
• Measurement & Attribution Protocol — learning-phase noise, the control rule, and separating an observed change from a plausible cause when reading in-flight movement.
• budget-optimizer — sets the initial allocation and owns the bid-pacing/learning-phase mode; this skill hands a fired reallocation trigger to it.
• ad-account-auditor — the auditor-class gate that computes the RQS and runs the R1/R2/O1/O2/A1 vetoes; this skill does not score.
• scripts/connectors/README.md —

  ledger.py

  record / trend reference.
• CONNECTORS.md · SECURITY.md —

  ~~ad platform

  own-data export recipe and the untrusted-data boundary.