Report Generation

This skill enables an AI agent to produce polished, data-driven reports from structured input. The agent accepts data in JSON, CSV, or API response format, applies a report template, generates narrative insights alongside tables and chart specifications, and outputs the final report in Markdown, HTML, or PDF. It supports common report types including sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems.

Workflow

1. Ingest and validate the source data. Accept the input data file or payload in JSON, CSV, YAML, or raw API response format. Validate the schema — check for required fields, correct data types, missing values, and outliers. If the data is incomplete (e.g., a sprint retro JSON missing the velocity field), flag the gap and either infer a default, request the missing value, or note the omission in the report. Normalize date formats, currency symbols, and units for consistency.

2. Select or customize the report template. Match the data to a report template based on the report type specified by the user. Built-in templates include: sprint_retrospective, financial_summary, analytics_dashboard, incident_postmortem, and weekly_status. Each template defines the section order, required data mappings, chart types, and tone (analytical for financial reports, constructive for retros, urgent for postmortems). Users can customize templates by overriding sections, adding fields, or changing the visual theme.

3. Compute metrics and generate insights. Derive computed metrics from the raw data — percentage changes, averages, rankings, trend directions, and anomaly flags. For a sprint retro, calculate velocity variance and commitment accuracy. For an analytics report, compute conversion rates and segment-level breakdowns. Then generate narrative insights: not just "conversion rate was 3.2%" but "conversion rate dropped 0.8pp from last period, driven primarily by a 15% decline in mobile traffic."

4. Populate the report structure. Fill the template sections with the computed metrics, narrative insights, and formatted data tables. Generate chart specifications (bar, line, pie, or table) for each visualization slot in the template. Use Markdown table syntax for inline tables and a chart spec format (e.g., Mermaid, Vega-Lite, or Chart.js JSON) for visual charts. Add section headers, executive summary, and any appendices.

5. Format and render the output. Produce the final report in the requested format. For Markdown, output a clean .md file with tables and chart code blocks. For HTML, wrap the Markdown output in a styled template with CSS for print-friendly rendering. For PDF, convert the HTML version using a headless browser or a tool like Puppeteer or WeasyPrint. Ensure charts render correctly in all formats and that tables don't break across pages in PDF.

6. Review and finalize. Run a quality check: verify all data points in the report trace back to the source data, ensure no template variables remain unresolved (e.g., {{quarter}}), confirm that insights are supported by the numbers, and check that the report length matches the expected range for its type. Deliver the report with a brief summary of what was generated.

Usage