Skill: matthewxfz3/email-news-digest (version 1.0.0)

--- 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: ```bash skills/email-news-digest/scripts/process_and_send.sh \ --recipients "[REDACTED_SECRET],[REDACTED_SECRET]" \ --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](references/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 * [email-filters.md](references/email-filters.md): Provides examples of Gmail search operators. * [html-template.html](references/html-template.html): The HTML structure used for the email report.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)