Individual skill
ads-monitor
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.
skills/ads-monitor/SKILL.md
Source commit fc3908e786c318776df89a3a098333da377364be
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 10,931
- Repository last updated
- Nov 1, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- claude-code Repository topic
- claude-code-skill Repository topic
- ai-marketing Repository topic
- ai Repository topic
- marketing-automation Repository topic
- open-source Repository topic
- agent-skills Repository topic
- google-ads Repository topic
- meta-ads Repository topic
- paid-advertising Repository topic
- marketing Derived category
- finance Derived category
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 1,243 bytes
Read excerpt
# 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.