--- 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 "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](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. --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/matthewxfz3/email-news-digest/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/matthewxfz3/email-news-digest/SKILL.md Attribution basis: Source repository record Fields of work: Marketing Roles: Content Marketer, Email Marketer, Marketing Analyst Specialties: API Tools: Gmail, Nano Banana Pro Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: fab221415f635a99feeed10d249b7b206b7c46235b0e178655fc530bcd801722