Automates the analysis of dashboards for anomalies and trends in the Strategy Cloud library.

Description

Browses the Strategy Cloud library, inventories all dashboards, opens each one, and detects anomalies, threshold breaches, and trends using the detection patterns defined in references/finding-patterns.json.

Trigger

This skill is invoked automatically when a compliance or strategy review needs dashboard analysis, or when orchestrated by user-invoked commands.

Instructions

Query Guidelines

Use natural language, not SQL. Both Strategy AI agents and the MCP USL accept natural language queries. Write questions the way you would ask a colleague (e.g., "Show me dashboards with declining revenue trends").
Only use tables in the playground schema. All queries should target the playground schema exclusively. Do not reference tables outside of it.
Default environment and dashboard. For dashboard viewing and analysis (this skill), use demo.microstrategy.com as the default environment and the Office Royale Sales dashboard unless otherwise specified. For all other analysis (data queries, reporting, etc.), use the Strategy USL and Strategy Agent MCPs directly.
Tool routing fallback. If no specific project or assignee is configured for an enterprise tool, discover the best-fit project or workspace in that tool automatically rather than skipping the routing.
Missing MCP servers. If an enterprise tool's MCP server is not connected, skip that tool's routing step. Dashboard analysis should always run regardless — only the downstream enterprise routing is skipped.

1. Discover — Browse the Strategy Library

Use the Strategy MCP server to access the Strategy Cloud library:

Navigate the library folder structure (typically organized by LOB: Sales/, Marketing/, Finance/, Operations/, HR/, etc.)
Inventory every dashboard: capture name, folder path, last modified date, and dashboard URL
Aim for 100% coverage — do not sample; analyze every dashboard

2. Analyze — Open Each Dashboard

For each inventoried dashboard:

Open/query the dashboard to retrieve its visualizations and data
Review systematically: left-to-right, top-to-bottom across all visualizations
Apply the detection patterns from references/finding-patterns.json:

Anomaly Detection:

Spikes: Value increase >20% vs prior period
Drops: Value decrease >15% vs prior period
Outliers: Data points beyond 2 standard deviations from mean
Gaps: Missing data periods (null or absent values)

Threshold Breaches:

Red/critical status indicators
Yellow/warning indicators approaching threshold
Target misses with negative variance (>10% for warning, >20% for critical)
Declining trend arrows

Trend Patterns:

3+ consecutive periods moving in the same direction
Acceleration (rate of change increasing)
Reversal (direction change after established trend)

Comparisons:

Year-over-year variance >15%
Actual vs budget/forecast variance >10%
Regional or product peer variance >20%

3. Document Findings

For each detected finding, capture:

{
  "dashboard_name": "...",
  "dashboard_url": "...",
  "dashboard_folder": "...",
  "finding_type": "anomaly|threshold_breach|trend|comparison",
  "severity": "critical|warning|informational",
  "metric": "Name of the affected metric",
  "current_value": "Current metric value",
  "comparison_value": "Prior period / target / peer value",
  "variance_percent": 0.0,
  "details": "Human-readable description of what was found",
  "visualization_source": "Name/position of the chart or table"
}

4. Return Results

Return a JSON object:

{
  "analyzed_at": "ISO-8601 timestamp",
  "dashboards_inventoried": 0,
  "dashboards_analyzed": 0,
  "coverage_percent": 100,
  "total_findings": 0,
  "findings_by_severity": {
    "critical": 0,
    "warning": 0,
    "informational": 0
  },
  "findings": [ ... ]
}

Error Handling

If a dashboard fails to load, log the error and continue with remaining dashboards
Report coverage percentage based on successfully analyzed dashboards
Note any inaccessible dashboards in the output

analyze-dashboards