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anomaly-scan

Prix non confirmé★ 787 Stars GitHubRegistre mis à jour · 2 sept. 2026agent-skill

Vue d’ensemble

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.

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/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 "${CLAUDE_PLUGIN_ROOT}/scripts/execution-tracker.py" --brand {slug} --action get-history --limit 14 to correlate anomalies with recent changes — did a campaign launch, pause, budget shift, creative swap, landing page change, or audience expansion precede the anomaly?
Cross-reference with known factors: Check for known platform outages, algorithm updates (Google core updates, Meta policy changes), industry events, seasonal patterns, and any user-provided known events that could explain the deviation.
Classify anomalies by severity: Critical (revenue-impacting, requires immediate action — tracking broken, CPA 3x+ baseline, budget overspend >20%, deliverability below 80%), Warning (significant deviations worth investigating within 24 hours — traffic down 30%+, engagement halved, CTR dropped 40%+), or Info (notable but non-urgent — gradual trend shifts, minor CPA increases, seasonal patterns emerging).
Determine probable causes: For each anomaly, analyze root causes using the diagnostic framework from skills/analytics-insights/anomaly-diagnosis.md. Categorize as data/tracking issue, external factor (algorithm update, competitor action, seasonal shift), internal change (campaign modification, landing page update), or platform change (policy update, feature deprecation, auction dynamics shift).
Save critical anomalies as insights: For critical and warning-level anomalies, persist via python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action save-insight --data '{"type":"anomaly","insight":"...","context":"..."}' so they are tracked, surface in future reports, and can be referenced in post-mortems.

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

Voir la source ↗

Métadonnées du fichier

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."

Voir le texte original

---
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

1. **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.
2. **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.
3. **Load historical baselines**: Execute `python "${CLAUDE_PLUGIN_ROOT}/scripts/performance-monitor.py" --brand {slug} --action get-baseline`
   to retrieve rolling averages, standard deviations, and expected ranges for each metric. If no baseline exists yet,
   use the comparison period data to establish a temporary baseline and note this in the output.
4. **Run anomaly detection**: Execute `python "${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.
5. **Cross-reference with recent executions**: Check execution history via
   `python "${CLAUDE_PLUGIN_ROOT}/scripts/execution-tracker.py" --brand {slug} --action get-history --limit 14`
   to correlate anomalies with recent changes — did a campaign launch, pause, budget shift, creative swap,
   landing page change, or audience expansion precede the anomaly?
6. **Cross-reference with known factors**: Check for known platform outages, algorithm updates
   (Google core updates, Meta policy changes), industry events, seasonal patterns, and any user-provided
   known events that could explain the deviation.
7. **Classify anomalies by severity**: Critical (revenue-impacting, requires immediate action — tracking broken,
   CPA 3x+ baseline, budget overspend >20%, deliverability below 80%), Warning (significant deviations worth
   investigating within 24 hours — traffic down 30%+, engagement halved, CTR dropped 40%+), or Info (notable
   but non-urgent — gradual trend shifts, minor CPA increases, seasonal patterns emerging).
8. **Determine probable causes**: For each anomaly, analyze root causes using the diagnostic framework from
   `skills/analytics-insights/anomaly-diagnosis.md`. Categorize as data/tracking issue, external factor
   (algorithm update, competitor action, seasonal shift), internal change (campaign modification, landing page
   update), or platform change (policy update, feature deprecation, auction dynamics shift).
9. **Save critical anomalies as insights**: For critical and warning-level anomalies, persist via
   `python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action save-insight --data '{"type":"anomaly","insight":"...","context":"..."}'`
   so they are tracked, surface in future reports, and can be referenced in post-mortems.

## 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

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Licence: MIT
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Licence: MIT

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review

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Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
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Source et conseils d’utilisation

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Dépôt source: indranilbanerjee/digital-marketing-pro
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 17 août 2026
Registre mis à jour: 2 sept. 2026
Chemin des instructions: skills/anomaly-scan/SKILL.md @ fa4ccd0a4afc

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

73/100

Solide

Confiance

72/100

Sandbox uniquement

Audit

82/100

Risqué

Ouvrir l’audit complet Voir le rapport d’évaluation

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review

Verified installs: —
Résultats: —

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    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 82,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 73,
    "label": "Strong"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Data analysis",
    "maintenance": "2mo since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "Audit risk risky exceeds max_risk=medium",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
    "Financial research output is not financial advice; require human review before any live investment decision.",
    "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."
  ],
  "agent_contract": {
    "task_input": "Use anomaly-scan in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 80/100 Strong shortlist",
      "Audit: 82/100 Risky",
      "Safety: 66/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "indranilbanerjee-anomaly-scan (anomaly-scan)",
      "install_command": "npx skills add indranilbanerjee/digital-marketing-pro --skill anomaly-scan",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "indranilbanerjee-anomaly-scan",
      "task": "Use anomaly-scan in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/indranilbanerjee-anomaly-scan",
    "api": "https://www.openagentskill.com/api/agent/skills/indranilbanerjee-anomaly-scan",
    "audit": "https://www.openagentskill.com/skills/indranilbanerjee-anomaly-scan/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=indranilbanerjee-anomaly-scan&task=Use%20anomaly-scan%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20anomaly-scan%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20anomaly-scan%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/indranilbanerjee-anomaly-scan/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/indranilbanerjee-anomaly-scan"
  }
}

Pour le créateur

Source de la fiche

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: indranilbanerjee
Source: indranilbanerjee/digital-marketing-pro
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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