| name | anomaly-scan |
| description | Scan all connected marketing platforms for statistically significant deviations from stored baselines — traffic drops, CPA spikes, deliverability collapse, budget overruns, or unexpected wins — classified critical/warning/info with probable causes, correlation to recent changes, and recommended actions. Triggers on "/digital-marketing-pro:anomaly-scan", "why did our CPA spike", "did anything weird happen this week", "check for anomalies", "our conversions suddenly dropped". Runs performance-monitor.py for baselines and detection, correlates flags against execution-tracker.py history and the diagnostic framework in skills/analytics-insights/anomaly-diagnosis.md, and persists critical findings as insights via campaign-tracker.py. Reads the brand profile. |
/digital-marketing-pro:anomaly-scan
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
Process
- Load brand context: Read
~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
- Pull current metrics from all connected MCPs: Query each connected analytics platform
(google-analytics, google-ads, meta-marketing, linkedin-marketing, tiktok-ads, mailchimp, stripe, mixpanel,
amplitude, shopify, etc.) for all available metrics across the specified scan period. Include traffic, spend,
conversions, CPA, ROAS, engagement rates, deliverability, and revenue metrics.
- Load historical baselines: Execute
python "${CLAUDE_PLUGIN_ROOT}/scripts/performance-monitor.py" --brand {slug} --action get-baseline
to retrieve rolling averages, standard deviations, and expected ranges for each metric. If no baseline exists yet,
use the comparison period data to establish a temporary baseline and note this in the output.
- Run anomaly detection: Execute
python "${CLAUDE_PLUGIN_ROOT}/scripts/performance-monitor.py" --brand {slug} --action detect-anomalies --data '{...current-period metrics...}'
to flag metrics that fall outside the expected ranges computed from the stored baseline (mean ± standard deviations).
Apply day-of-week and seasonality adjustments where historical data supports it.
- Cross-reference with recent executions: Check execution history via
python "${CLAUDE_PLUGIN_ROOT}/scripts/execution-tracker.py" --brand {slug} --action get-history --limit 14
to correlate anomalies with recent changes — did a campaign launch, pause, budget shift, creative swap,
landing page change, or audience expansion precede the anomaly?
- Cross-reference with known factors: Check for known platform outages, algorithm updates
(Google core updates, Meta policy changes), industry events, seasonal patterns, and any user-provided
known events that could explain the deviation.
- Classify anomalies by severity: Critical (revenue-impacting, requires immediate action — tracking broken,
CPA 3x+ baseline, budget overspend >20%, deliverability below 80%), Warning (significant deviations worth
investigating within 24 hours — traffic down 30%+, engagement halved, CTR dropped 40%+), or Info (notable
but non-urgent — gradual trend shifts, minor CPA increases, seasonal patterns emerging).
Output
A structured anomaly report containing:
- Scan summary: Platforms scanned, time period analyzed, sensitivity level used, baseline period,
total anomalies detected (by severity), and overall marketing health assessment (healthy, caution, or critical)
- Critical anomalies (if any): Metric name, platform, expected range (mean +/- threshold), actual value,
deviation magnitude (in std devs and percentage), probable cause, estimated revenue impact, and recommended
immediate action
- Warning anomalies: Same structure as critical, with recommended investigation steps and a 24-hour
action plan for each
- Info anomalies: Notable deviations worth monitoring with watch criteria — what to look for to determine
if the trend continues or reverses
- Correlation analysis: Connections between anomalies and recent execution history — which changes may have
caused which deviations, with confidence levels (strong, possible, unlikely)
- Platform health summary: Per-platform health indicator (green/yellow/red) based on the number and severity
of anomalies detected, plus a trend vs the last scan if previous scan data exists
- Recommended actions: Priority-ordered list of responses — immediate fixes for critical issues, investigations
for warnings, monitoring adjustments for info items, and any baseline recalibrations needed
- Baseline update notes: Whether any baselines need recalibration due to structural changes (e.g., new campaign
launched, channel added, seasonal shift, or pricing change that permanently alters expected ranges)
Agents Used
- performance-monitor-agent — Anomaly detection engine, baseline management, statistical threshold evaluation, historical trend analysis, severity classification, and seasonality adjustment
- analytics-analyst — Root cause interpretation, cross-platform correlation, contextual analysis (seasonality, algorithm updates, competitive shifts), impact estimation, and actionable recommendation generation
