Ads Monitor

by agricidanielac2164493391No license9.8K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated yesterday

Monitor paid-ad account pacing, delivery, performance, creative fatigue, tracking, policy, and data quality across supported platforms. Use for daily or weekly checks, anomaly review, budget pacing, post-launch verification, or campaign monitoring.

AI-generated overview

Monitors paid-ad accounts for pacing, delivery, performance, creative fatigue, tracking, policy and data-quality issues.

What it does
This skill defines a read-only monitoring procedure for paid media accounts. It compares two or more normalized snapshots with compatible account, timezone, currency, metric and attribution definitions, checks data freshness and finalization windows, and separates expected learning, seasonality, reporting latency and planned changes from unexplained anomalies. It evaluates pacing, delivery, conversion quality, unit economics, creative fatigue, tracking health, policy status and changed account objects, then returns observations, confidence, likely causes, required investigation and decision thresholds, persisting a versioned monitoring bundle and linking detected failures to regression or…
When to use it
Use it for daily or weekly paid-ad checks, anomaly review, budget pacing, post-launch verification or ongoing campaign monitoring. It suits situations where snapshots from two or more periods must be compared and noise distinguished from material change.
Requirements
Instructions only; no scripts are shipped. It needs two or more normalized snapshots with compatible account, timezone, currency, metric and attribution definitions, plus a place to persist a versioned monitoring bundle and link failures to follow-up tasks.

Paid Media Monitoring

  1. Load two or more normalized snapshots with compatible account, timezone, currency, metric, and attribution definitions.
  2. Validate data freshness and finalization windows before comparing periods.
  3. Separate expected learning, seasonality, reporting latency, and planned changes from unexplained anomalies.
  4. Evaluate pacing, delivery, conversion quality, unit economics, creative fatigue, tracking health, policy status, and changed account objects.
  5. Return observations, confidence, likely causes, required investigation, and decision thresholds. Do not mutate the account.
  6. Persist a versioned monitoring bundle and link detected failures to regression or follow-up tasks.

Do not alert on percentage changes with trivial denominators or incomparable windows. State when evidence cannot distinguish noise from a material change.

Source and attribution

Source:agricidaniel/claude-adsinskills/ads-monitorat commitac21644

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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