name: analytics-reporting
description: Use for ad campaign performance analysis, weekly trend reporting, data correlation, and actionable optimization insights.

Analytics Reporting

Overview

This skill guides the execution of precise, data-driven campaign performance analysis. It ensures accuracy, proper sourcing, and actionable insights for weekly trends and optimization.

When to Use

• Analyzing ad campaign performance
• Generating weekly trend reports
• Correlating data from multiple sources
• Seeking actionable optimization insights
• Validating analytics reports

Core Principles

• Data Accuracy First: Never manually aggregate raw data. Calculations must be precise and reproducible.
• Source Everything: Cite the file and row for every single metric reported.
• Use User-Specified Data: Use the primary data file specified for all main metrics. Use secondary files only for anomalies.
• Show Your Work: Reveal formulas and steps for derived metrics.
• Actionable Insights: Convert every metric and finding into a specific, owner-assignable action.
• Context is Key: Do not start without confirming data sources and key metrics.

Workflow

1. Context Gathering (Setup)

Goal: Gather verified quantitative context before computation.

1. Check Configuration: Ensure data sources and metrics are set.
2. Elicit Information:
   • Path to primary data file (e.g., weekly totals).
   • Optional secondary data file (e.g., daily results).
   • Optional changelog file.
   • List of primary metrics (e.g., "Subscriptions, Revenue, DAU").
   • Main funnel stages if applicable.
3. Load & Verify: Load specified files and check for basic integrity (headers, accessibility).
4. Data Quality Alert: If data is missing/inaccessible, issue a clear alert and stop.

2. Analysis Execution

• Pre-Analysis: Briefly state purpose and steps (e.g., "Validating data → Analyzing trends...").
• During Analysis: Narrate progress succinctly after each major calculation.
• Persistence: Continue until a complete, validated report or alert is produced. Never infer missing data.

3. Reporting

• Structure:
  • Summary
  • Findings
  • Correlations
  • Recommendations
  • Sources
• Formatting:
  • Use backticks for metric names, campaign names, file names.
  • Use code fences ``` for calculations and citations.
  • Metric Format: [Metric Name] +19.6% (920→1,100, W5→W6, [file_name.csv] row 15).

4. Validation

• Verify all sources are cited.
• Ensure all findings lead to recommendations.

Output Policy

• Location: ALWAYS create reports in docs/analytics/.
• Prohibition: NEVER write to internal AI directories for reports. Use project-specific output locations.

Common Mistakes

• Manually aggregating data without showing the formula.
• Forgetting to cite the specific row/file for a number.
• Producing a report without actionable recommendations.