Anomaly Scan
Scan connected platforms for anomalies against stored baselines, with likely causes.
Install
npx skills add indranilbanerjee/digital-marketing-pro --skill anomaly-scan -g -a claude-code -y- Repository
- indranilbanerjee/digital-marketing-pro921
- Category
- Engineering
- Updated
- 6d ago
- License
- MIT
- Context cost
- Mediumabout 1,830 tokens
- Runs scripts
- No, instructions only
What it does
Scan connected platforms for anomalies against stored baselines, with likely causes. \"why did our CPA spike\
/digital-marketing-pro:anomaly-scan
Script location. If your host does not set
${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin'sscripts/folder, next toskills/. Run them with Python 3.10 or newer:python, orpython3wherepythonis not found (macOS, many Linux systems).
Purpose
Scan all connected marketing platforms for anomalies, statistically significant deviations from established baselines that could indicate problems (traffic drops, CPA spikes, deliverability collapse, budget overruns) or opportunities (viral content, conversion rate improvements, unexpected channel growth). Designed to catch issues early, before they compound into costly problems, and to surface wins worth amplifying.
Input Required
The user must provide (or will be prompted for):
- Sensitivity level: Strict (flags deviations >1.5 standard deviations from baseline), normal (>2 std dev), or relaxed (>3 std dev). Defaults to normal
- Time period: The window to scan for anomalies, today, last 3 days, last 7 days, last 30 days, or custom range. Defaults to last 7 days
- Platforms (optional): Specific platforms to focus the scan on (e.g., "Google Ads and Meta only"). If omitted, all connected platforms are scanned
- Metrics focus (optional): Specific metrics to prioritize (e.g., "CPA and conversion rate only"). If omitted, all available metrics are evaluated
- Baseline period (optional): Custom baseline for comparison instead of the default. Defaults to the rolling 30-day average maintained by performance-monitor.py
- Exclude known events (optional): List of known events to filter out (e.g., "Black Friday sale", "site migration on Jan 15") so expected deviations are not flagged as anomalies
