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LLM Prompt: Family Daily Briefing

What it is

The "Family Daily Briefing" is a structured LLM prompt designed to synthesize data from multiple household services into a concise, actionable morning summary. It acts as a personalized "morning news" for the family, delivered via chat or email.

What problem it solves

Managing a household involves tracking disparate information across calendars, task managers, and weather apps. Checking each individually is time-consuming and often leads to missing important details. This prompt automates the synthesis, highlighting conflicts and priorities in a single, easy-to-read message.

Where it fits in the stack

This prompt is part of the AI Service layer. It is typically executed by an LLM node (like Ollama, GPT-5.5, or Claude 4.8) within an Orchestration workflow (n8n), consuming data from the Productivity (Calendar/Tasks) and Environmental (Weather) layers. Modern integrations utilize the Model Context Protocol (MCP 3.0) to provide real-time, secure access to these data sources.

Typical use cases

  • Morning Routine Automation: Sending a briefing at 07:00 AM every morning.
  • Conflict Resolution: Identifying and alerting the family if two members have overlapping commitments.
  • Activity Planning: Using the weather summary to suggest outdoor vs. indoor activities for the day's tasks.

Strengths

  • Centralization: Consolidates multiple data sources into one location.
  • Personalization: The tone and focus can be adjusted to suit the family's preferences.
  • Context Awareness: Can correlate tasks with calendar events (e.g., "Don't forget the library books since you are going to the mall nearby").

Limitations

  • Data Freshness: Relies on the n8n workflow fetching the latest data at the time of execution.
  • LLM Cost/Latency: Depending on the model used, there may be a small cost or a few seconds of delay in generating the briefing. Frontier models like GPT-5.5 or Claude 4.8 are faster but more expensive.
  • Hallucination Risk: Small chance of misinterpreting times or priorities if the input data is messy. Local models like Llama 4 Maverick can mitigate privacy concerns but may have higher latency on modest hardware.

When to use it

  • When your family uses multiple digital tools to manage life and needs a unified view.
  • When you want to gamify or encourage the completion of daily chores.
  • To start the day with a "human-like" touch through the inclusion of memories.

When not to use it

  • For families with extremely static schedules that don't change day-to-day.
  • If you have concerns about sharing personal calendar data with external LLM providers (use a local Ollama instance instead).
  • If your source systems (Calendar/Tasks) are not consistently updated.

Getting started

To implement the Family Daily Briefing: 1. Ensure your household data sources (Google Calendar, Vikunja, OpenWeatherMap) are accessible via n8n. 2. Use the Aggregate node in n8n to combine the data into a single JSON object. 3. Pass this object into the LLM prompt template provided below.

Prompt Template

# Role
You are the "Family Admin Assistant," a helpful, concise, and cheerful AI agent responsible for preparing the morning briefing for the family.

# Context
Today is {{ $today_date }}.
The weather today is {{ $weather_summary }}.

# Input Data
## Calendar Events (Google/Proton Calendar)
{{ $calendar_events }}

## Chores & Tasks (Vikunja/Habitica)
{{ $tasks }}

## "On This Day" Memories (Immich/Paperless)
{{ $memories }}

# Instructions
1. **Greeting**: Start with a warm, brief greeting and a mention of today's date and weather.
2. **Schedule**: Summarize the day's calendar events chronologically. Highlight any potential conflicts or busy periods.
3. **Tasks**: List the top 3-5 priority chores or tasks for today.
4. **Memories**: Briefly mention one "On This Day" memory to start the day with a smile.
5. **Tone**: Keep it helpful, concise, and upbeat. Avoid long-winded explanations.

# Output Format
Markdown-formatted text, suitable for delivery via Telegram or Email.

CLI examples

You can test the synthesis logic using the ollama CLI with a local model.

# Testing the briefing with Ollama and Llama 4 Maverick
ollama run llama-4-maverick "Prepare a family briefing for 2026-06-26. Weather: Sunny, 25C. Tasks: Buy milk, Fix sink. Events: Dentist at 2PM."

API examples

The briefing can be generated via a POST request to an LLM provider's API.

# Example API call to OpenAI (GPT-5.5) for briefing generation
curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5.5-preview",
    "messages": [
      {"role": "system", "content": "You are a helpful family assistant."},
      {"role": "user", "content": "Synthesis today'\''s data: [JSON DATA HERE]"}
    ]
  }'
  • Google Calendar: Primary data source for the schedule.
  • Vikunja: Primary data source for tasks and chores.
  • Habitica: Gamified task management alternative.
  • Immich: Source for "On This Day" photo memories.
  • Paperless-ngx: Source for "On This Day" document memories.
  • n8n: The workflow engine that runs the entire process.
  • Ollama: Recommended for private, local execution.
  • MCP — Standardized protocol for model-tool interaction.

Sources / references

Contribution Metadata

  • Last reviewed: 2026-06-26
  • Confidence: high