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run-paid-campaigns-weekly-read

Read mature paid campaign results for the weekly campaign workflow. Compare spend and outcomes with allowable costs, account for attribution windows and data coverage, and produce an evidence table and ad scorecard to inform later decisions. This is a step within a larger playbook.

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Step of the run-paid-campaigns playbook: reads each platform's last mature results window against the allowable cost, as evidence for the week's moves. Run run-paid-campaigns instead; not for direct use.

gooseworks-ai/goose-skills1,272 stars(GitHub 1,272 + cccskills 0)

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See how to install

Good for

  • Compare campaign results with allowable costs
  • Review ad level performance for the weekly report
  • Prepare evidence before drafting campaign changes

Files included (2)

  • SKILL.md2.1 KB
  • skill.meta.json228 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Weekly read (atom)

Load: ../run-paid-campaigns/references/reading-results.md.

Inputs: the volume regime; meta_read view: performance (a 14-day and a 3-day window, ranked by spend) for Goose ads, when Meta is chosen and GooseWorks is connected (local mode: the connector's date-range reads, ../run-paid-campaigns/references/local-mode.md); the marketer's exports for non-Goose Meta and other platforms; analyze-performance's weekly read (blended MER, new-customer CAC, store-joined CPA per angle, its kill/scale/hold calls); decisions.md (edits inside the window); the brief's test plan and targets.

Do:

  1. Set the window for the regime (high-volume: 14-day minus the immature last 3 days; low-volume: 14–28 days matured past the qualified lag; app: 7–14 days past postback delay); same attribution setting as last week. Trends from trailing totals are approximate — say so.

  2. State coverage: share of spend visible per platform; what is not visible (frequency, learning status, CPM unless exported).

  3. Per ad set / ad, sorted by spend: spend, results, cost per result vs allowable, CTR trend over 3 weeks, conversions (n) and its noise band (±1/√n), learning (events/week vs ~50), and any edit inside the window. Traffic tests: CTR, cost per LPV, UTM store CVR.

  4. Join to the blended read: does store-joined data agree with the platform? If not, the store-joined number decides and the disagreement is stated.

  5. Mark each object: readable winner · readable loser · near-threshold · too early · broken.

  6. Build the per-ad scorecard (../run-paid-campaigns/references/run-memory.md § End): top 10 by spend plus all others, each row against its row in the last run record.

Output: the evidence table the moves are drafted from, with confidence per row, and the scorecard. No moves yet.

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