Weekly Automation Digest
Automated weekly team update via n8n + Slack
The prompt
Summarize this week's data into a short digest for the team. Input: {{$json.metrics}}. Output format: 3 bullet highlights, 1 concern, 1 suggested action. Keep the whole digest under 100 words so it fits in a Slack message.What to replace
Swap these placeholders for your own details before running the prompt:
{{$json.metrics}}your own value
How to use this prompt
- Copy the prompt using the button above.
- Replace {{$json.metrics}} with your own details — the more specific you are, the better the output.
- Paste it into n8n + ChatGPT and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Related prompts
RSS-to-Social Repurposer
Auto-posting new articles to social channels
ChatGPTCritique-and-Revise Loops — ChatGPT
A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.
ClaudeCritique-and-Revise Loops — Claude
A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.
ChatGPT / Agent toolsThe ReAct Pattern (Reason + Act)
ReAct loops interleave reasoning ("what should I do next?") with actions (calling a tool, searching, running code), then feed the result back in before reasoning again.
ClaudeGiving Loops Memory
For loops that run over many steps (research agents, multi-turn assistants), summarize prior steps into a short running memory instead of replaying the full history every time — this keeps context small and cheap..
Any modelPlanner–Worker–Reviewer Teams
The most robust agent architecture is also the simplest: a Planner decomposes the goal into tasks, Workers execute one task each with narrow context, and a Reviewer checks results against acceptance criteria before anything ships.