| name | email-news-digest |
| description | Summarize recent emails, generate a thematic image, and send a formatted HTML email report with the summary and image. Use for daily news digests, project updates, or any email-based reporting that needs visual enhancement and rich formatting. |
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
- Email Retrieval: Fetches the most recent email matching your query.
- 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.)
- Image Generation: Creates a thematic image using the
nano-banana-pro skill based on your image-prompt.
- HTML Report Assembly: Constructs a dynamic HTML email body using a template, incorporating the summary and a reference to the generated image.
- 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
