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Scheduled report

On a schedule, query the database, crunch numbers with pandas, let AI write the analysis, render a PDF and email it to stakeholders.

The classic "Monday 8am report" — fully automated, and the prose is written fresh each time from the actual numbers.

Analytics feeding a report

The pattern

Trigger (schedule) → Database → Pandas Processor → LLM Agent (writes analysis)
                                                   → PDF Generator → Email

Step by step

  1. Trigger (Base) — scheduled execution (e.g. weekly). An external cron hitting the webhook URL works too.
  2. Database (Data) — the query for the period: orders, tickets, signups.
  3. Pandas Processor (Data) — aggregate: totals, growth vs. previous period, top items.
  4. LLM Agent (AI) — objective: "Write an executive summary of these metrics: what changed, why it matters, what to watch. Max 300 words, no fluff." Prompt: {{pandas_processor_1.result}}.
  5. PDF Generator (Data) — a template with brand header, the metrics table and the agent's summary via variables.
  6. Email (Applications) — send with the PDF attached to the stakeholder list.

Variations

  • Add a Chart node and attach the rendered graph to the PDF.
  • Post the summary to Slack through an MCP integration besides the email.
  • Source from an external warehouse (Postgres, BigQuery) via integrations instead of the Database node.

Genius prompt: "Create a weekly report flow: query my database, aggregate with pandas, have AI write the summary, generate a PDF and email it to the team."

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