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email-news-digest

Summarize recent emails, generate a thematic image

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Email News Digest

This skill automates the process of creating an AI-powered news digest from your recent emails, generating a relevant image, and sending a formatted HTML report.

Usage

To use this skill, run the process_and_send.sh script with the required parameters:

skills/email-news-digest/scripts/process_and_send.sh \
    --recipients "matthewxfz@gmail.com,salonigoel.ssc@gmail.com" \
    --email-query "newer_than:2d subject:news" \
    --image-prompt "A sharp, modern western style image representing AI growth, fierce competition, and diverse applications."

Parameters

  • --recipients: Comma-separated list of email addresses to send the digest to.
  • --email-query: Gmail search query to filter recent emails (e.g., "newer_than:2d subject:AI"). See email-filters.md for more examples.
  • --image-prompt: A descriptive prompt for the AI image generation.

How it Works

  1. Email Retrieval: Fetches the most recent email matching your query.
  2. Content Summarization: Extracts content and generates a structured summary (TL;DR, main title, and sections) using an internal Python script. (Note: The summarization script currently uses a placeholder summary; future enhancements will integrate a full LLM for dynamic summarization.)
  3. Image Generation: Creates a thematic image using the nano-banana-pro skill based on your image-prompt.
  4. HTML Report Assembly: Constructs a dynamic HTML email body using a template, incorporating the summary and a reference to the generated image.
  5. Email Dispatch: Sends the formatted HTML email with the image as an attachment using gog gmail send, employing a robust Base64 encoding/decoding method to handle complex HTML content safely.

Summarization Standards

To ensure high-quality output, the summarization process within this skill adheres to the following standards:

  • Key Insights & Trends: Prioritize extracting major announcements, significant developments, and overarching trends rather than mere factual recitations.
  • Conciseness: The TL;DR should be 3-4 sentences, providing a quick overview. Detailed sections should elaborate succinctly.
  • Accuracy & Fidelity: Summaries must faithfully represent the original content without introducing new information or distorting facts.
  • Clarity & Professionalism: Use clear, straightforward, and professional language. Avoid jargon where simpler terms suffice.
  • Bias Neutrality: Summaries should be objective, presenting information as-is without injecting personal opinions or biases.

Implementation Standards (Summarization Component)

  • Modularity: The summarization logic resides in scripts/summarize_content.py to ensure it's self-contained and easily upgradable.
  • Input/Output: The script should accept raw email content (or extracted text) as input and output a structured JSON object containing the TL;DR, main title, and markdown-formatted sections.
  • Future LLM Integration: The current Python script uses a placeholder. Future development will focus on integrating a robust Large Language Model (LLM) API (e.g., Gemini) to perform dynamic, context-aware summarization based on these standards.

References