Analytics & Reporting Skill (Revenue Intelligence)

Revenue intelligence for AI agents — metrics, funnels, anomaly detection, forecasting, and dashboards. Don't just pull numbers — find the story, explain why, and recommend action.

Skill Standard MCP Server ADK-Rust Enterprise License

Revenue Impact

  • Funnel analysis finds where revenue leaks (conversion drop-offs)
  • Anomaly detection catches revenue drops within hours, not days
  • Forecasting enables accurate revenue planning
  • Change attribution explains WHY metrics moved (actionable, not just data)
Workflow Revenue Impact Tool Calls
Revenue Metrics Track growth 3-4
Funnel Analysis Find revenue leaks 2-3
Anomaly Detection Catch drops early 2-3
Forecasting Revenue planning 2-3
Dashboard Building Executive visibility 3-5
Change Attribution Root cause → action 2-3

Installation

git clone https://github.com/zavora-ai/skill-analytics-reporting.git \
  ~/.skills/skills/analytics-reporting

Requirements

Required: mcp-analytics (28 tools)

Revenue combos:

  • mcp-slack — anomaly alerts and daily digests
  • mcp-crm — funnel insights → sales actions
  • mcp-finance — verify analytics revenue matches books

Folder Structure

analytics-reporting/
├── SKILL.md                      # Main skill
├── assets/
│   └── funnel-report.md          # Funnel analysis template
├── references/
│   ├── tool-sequences.md         # 28 tools categorized
│   ├── cross-mcp-workflows.md    # Analytics + Slack + CRM + Finance
│   └── examples.md               # MRR, funnels, anomalies
├── README.md
└── LICENSE

Contributors

James Karanja Maina

License

Apache-2.0

Part of the ADK-Rust Enterprise skills ecosystem. Built with ❤️ by Zavora AI

How It Works

The Insight Principle

This skill doesn't just pull numbers — it answers "so what?" and "now what?" for every metric:

  1. What happened? (query_metric, compare_metric)
  2. Why? (explain_change, breakdown_metric)
  3. What's next? (forecast_metric, generate_insight_summary)
  4. What should we do? (specific recommendations)

Success Criteria

Metric Target
Insight quality Actionable recommendations, not just numbers
Anomaly speed Revenue drops caught within hours
Forecast accuracy Confidence intervals on all predictions