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analytics-reporting

Business
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Use for ad campaign performance analysis, weekly trend reporting, data correlation, and actionable optimization insights.

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/marketing/analytics-reporting-kyteapp-growth-agents-and-sk/SKILL.md

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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.