Report Generator

Generate professional data reports (HTML/PDF-ready) with KPI summaries, charts, tables, executive insights, and recommendations from CSV, Excel, or JSON data...

Security Scan

The skill's code, instructions, and requirements match its stated purpose (generating HTML reports from local CSV/XLSX/JSON data); nothing in the package requests unrelated credentials or external endpoints and the script confines file access to the current working directory.

Purpose & Capability

Name/description (report generation) align with the included assets: SKILL.md, templates, chart guidelines, and a Python script that reads local data and emits an HTML report with charts and KPIs. No unrelated capabilities or credentials are requested.

Instruction Scope

SKILL.md stays within the scope of report generation and explicitly points to the included script and reference templates. The runtime instructions and script only reference input data files and local output; they do not instruct reading unrelated system state or exfiltrating data.

Install Mechanism

There is no install spec (instruction-only plus a helper script). The provided Python script requires pandas and matplotlib to run, but the skill does not declare or install these dependencies—users must ensure the runtime environment has them. Lack of an install step is low risk but may cause runtime failures if dependencies are missing.

Credentials

The skill requests no environment variables, credentials, or config paths. The behavior is proportional: it operates on user-supplied files and writes output locally. There are no requests for unrelated secrets or external service access.

Persistence & Privilege

The skill is not forced always-on and does not modify other skills or system-wide configurations. It runs only when invoked and does not ask to persist credentials or enable autonomous always-running behavior.

Assessment

This package appears coherent and limited to generating local HTML reports. Before installing/using it: (1) inspect the scripts/generate_report.py file yourself (it's included and small); (2) run it in an isolated working directory—the script allows reading any file inside the current working directory, so avoid running it where sensitive files live; (3) ensure Python, pandas, and matplotlib are available in your environment (the skill doesn't install them); (4) test with sample data first to confirm outputs; and (5) if you need remote or automated execution, consider adding an explicit install step and dependency management (virtualenv/requirements) and review for any modifications.

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:
   • Executive summary
   • KPI dashboard
   • Detailed analysis
   • Charts/tables
   • Recommendations
6. 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.