Report Generator

Overview

Create clean, decision-ready reports from structured data files or user-described datasets. Prioritize business readability: clear KPIs, trend visuals, concise narrative insights, and practical recommendations.

Workflow

1. Validate input data source (CSV/XLSX/JSON or user-provided schema description).
2. Identify report goal and audience (executive summary vs operational detail).
3. Compute KPIs and trends relevant to the goal.
4. Generate visuals (bar + line at minimum; add breakdown charts as needed).
5. Produce formatted report sections in this order:
6. Executive summary
7. KPI dashboard
8. Detailed analysis
9. Charts/tables
10. Recommendations
11. Sanity-check numbers and narrative consistency before returning deliverable.

Report Blueprint

Use this canonical structure unless user asks otherwise:

report = {
    "title": "Monthly Sales Report",
    "period": "January 2024",
    "sections": [
        "executive_summary",
        "kpi_dashboard",
        "detailed_analysis",
        "charts",
        "recommendations",
    ],
}

KPI Defaults

Use these by default when fields exist; adapt names via user mapping when needed: - Revenue total / average - Order count and average order value - Growth rate (period-over-period) - Top category/product/customer by contribution - Trend direction (up/down/flat)

Output Rules

• Keep narrative concise and business-facing.
• Highlight 3-5 key findings max in executive summary.
• Flag missing/dirty data explicitly.
• Never claim causality without supporting data.

Implementation Resources

• Use scripts/generate_report.py for deterministic report generation.
• Use references/report-templates.md for section templates and phrasing patterns.
• Use references/chart-guidelines.md for chart selection and formatting standards.