agricidaniel/claude-ads

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.

First seen Jul 13, 2026

Installation

$ npx skills add agricidaniel/claude-ads --skill ads-monitor

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from agricidaniel/claude-ads · top by installs.

npx skills add agricidaniel/claude-ads

Browse all from agricidaniel/claude-ads

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 9.0K
License LICENSE
Default branch main
Open issues 19
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,174 B
  • docs SUMMARY.md 1,134 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 356 installs

SKILL.md

Paid Media Monitoring

  1. Load two or more normalized snapshots with compatible account, timezone,

currency, metric, and attribution definitions.

  1. Validate data freshness and finalization windows before comparing periods.
  2. Separate expected learning, seasonality, reporting latency, and planned changes

from unexplained anomalies.

  1. Evaluate pacing, delivery, conversion quality, unit economics, creative fatigue,

tracking health, policy status, and changed account objects.

  1. Return observations, confidence, likely causes, required investigation, and

decision thresholds. Do not mutate the account.

  1. 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.