Navinspire-ia Report Generator
Overview
A report = data + narrative + format. Automate the pipeline so the recurring version costs minutes, not days.
Report anatomy
- Executive summary - the 5 numbers and 3 messages a busy reader needs (written last)
- Sections - per topic: chart/table + 2-4 sentences of interpretation ("so what", not "the chart shows")
- Appendix - methodology, data sources + dates, detailed tables
Pipeline
sources (CSV/API/DB/notes) → collect (exec/`database-explorer`)
→ compute (pandas, `spreadsheet-analyst`)
→ charts (matplotlib → PNG)
→ narrative (drafted per section)
→ render (`pdf-generator` HTML route / `docx-generator` / Markdown)
Keep the whole pipeline as a script + config in reports/<name>/build/ so reruns are one command. The report is what you deliver; the pipeline is a build step the user only hears about when they ask for it.
Workflow
- Define with the user: audience, questions the report answers, sources, frequency, format.
- Build the pipeline; hardcode nothing that changes per period (dates, paths → parameters).
- First edition: validate numbers against sources manually; validate structure with the user.
- Recurring: schedule via
cron; each run regenerates data + charts, drafts fresh narrative on the new numbers, flags notable changes vs previous period. - Never ship silently - the narrative interpretation gets a human glance for sensitive reports.
Chart discipline
- One message per chart, stated in the chart title ("CA +18% porté par le segment public")
- Same scales/colors across periods for comparability; label directly, avoid legend hunting
Rules
- Every number traceable to a source and date; methodology in the appendix.
- Period-over-period comparison is mandatory - a number without reference is noise.
- Live KPI dashboards →
kpi-reporter; this skill produces documents.