{
  "schemaVersion": "1.0",
  "item": {
    "slug": "email-news-digest",
    "name": "Email News Digest",
    "source": "tencent",
    "type": "skill",
    "category": "内容创作",
    "sourceUrl": "https://clawhub.ai/matthewxfz3/email-news-digest",
    "canonicalUrl": "https://clawhub.ai/matthewxfz3/email-news-digest",
    "targetPlatform": "OpenClaw"
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    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=email-news-digest",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md",
      "references/email-filters.md",
      "references/html-template.html",
      "scripts/process_and_send.sh",
      "scripts/summarize_content.py"
    ],
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      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
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          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
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      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
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        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
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        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/email-news-digest"
    },
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        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/email-news-digest",
    "agentPageUrl": "https://openagent3.xyz/skills/email-news-digest/agent",
    "manifestUrl": "https://openagent3.xyz/skills/email-news-digest/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/email-news-digest/agent.md"
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  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Email News Digest",
        "body": "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."
      },
      {
        "title": "Usage",
        "body": "To use this skill, run the process_and_send.sh script with the required parameters:\n\nskills/email-news-digest/scripts/process_and_send.sh \\\n    --recipients \"matthewxfz@gmail.com,salonigoel.ssc@gmail.com\" \\\n    --email-query \"newer_than:2d subject:news\" \\\n    --image-prompt \"A sharp, modern western style image representing AI growth, fierce competition, and diverse applications.\""
      },
      {
        "title": "Parameters",
        "body": "--recipients: Comma-separated list of email addresses to send the digest to.\n--email-query: Gmail search query to filter recent emails (e.g., \"newer_than:2d subject:AI\"). See email-filters.md for more examples.\n--image-prompt: A descriptive prompt for the AI image generation."
      },
      {
        "title": "How it Works",
        "body": "Email Retrieval: Fetches the most recent email matching your query.\nContent 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.)\nImage Generation: Creates a thematic image using the nano-banana-pro skill based on your image-prompt.\nHTML Report Assembly: Constructs a dynamic HTML email body using a template, incorporating the summary and a reference to the generated image.\nEmail 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."
      },
      {
        "title": "Summarization Standards",
        "body": "To ensure high-quality output, the summarization process within this skill adheres to the following standards:\n\nKey Insights & Trends: Prioritize extracting major announcements, significant developments, and overarching trends rather than mere factual recitations.\nConciseness: The TL;DR should be 3-4 sentences, providing a quick overview. Detailed sections should elaborate succinctly.\nAccuracy & Fidelity: Summaries must faithfully represent the original content without introducing new information or distorting facts.\nClarity & Professionalism: Use clear, straightforward, and professional language. Avoid jargon where simpler terms suffice.\nBias Neutrality: Summaries should be objective, presenting information as-is without injecting personal opinions or biases."
      },
      {
        "title": "Implementation Standards (Summarization Component)",
        "body": "Modularity: The summarization logic resides in scripts/summarize_content.py to ensure it's self-contained and easily upgradable.\nInput/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.\nFuture 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."
      },
      {
        "title": "References",
        "body": "email-filters.md: Provides examples of Gmail search operators.\nhtml-template.html: The HTML structure used for the email report."
      }
    ],
    "body": "Email News Digest\n\nThis 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.\n\nUsage\n\nTo use this skill, run the process_and_send.sh script with the required parameters:\n\nskills/email-news-digest/scripts/process_and_send.sh \\\n    --recipients \"matthewxfz@gmail.com,salonigoel.ssc@gmail.com\" \\\n    --email-query \"newer_than:2d subject:news\" \\\n    --image-prompt \"A sharp, modern western style image representing AI growth, fierce competition, and diverse applications.\"\n\nParameters\n--recipients: Comma-separated list of email addresses to send the digest to.\n--email-query: Gmail search query to filter recent emails (e.g., \"newer_than:2d subject:AI\"). See email-filters.md for more examples.\n--image-prompt: A descriptive prompt for the AI image generation.\nHow it Works\nEmail Retrieval: Fetches the most recent email matching your query.\nContent 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.)\nImage Generation: Creates a thematic image using the nano-banana-pro skill based on your image-prompt.\nHTML Report Assembly: Constructs a dynamic HTML email body using a template, incorporating the summary and a reference to the generated image.\nEmail 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.\nSummarization Standards\n\nTo ensure high-quality output, the summarization process within this skill adheres to the following standards:\n\nKey Insights & Trends: Prioritize extracting major announcements, significant developments, and overarching trends rather than mere factual recitations.\nConciseness: The TL;DR should be 3-4 sentences, providing a quick overview. Detailed sections should elaborate succinctly.\nAccuracy & Fidelity: Summaries must faithfully represent the original content without introducing new information or distorting facts.\nClarity & Professionalism: Use clear, straightforward, and professional language. Avoid jargon where simpler terms suffice.\nBias Neutrality: Summaries should be objective, presenting information as-is without injecting personal opinions or biases.\nImplementation Standards (Summarization Component)\nModularity: The summarization logic resides in scripts/summarize_content.py to ensure it's self-contained and easily upgradable.\nInput/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.\nFuture 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.\nReferences\nemail-filters.md: Provides examples of Gmail search operators.\nhtml-template.html: The HTML structure used for the email report."
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/matthewxfz3/email-news-digest",
    "publisherUrl": "https://clawhub.ai/matthewxfz3/email-news-digest",
    "owner": "matthewxfz3",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/email-news-digest",
    "downloadUrl": "https://openagent3.xyz/downloads/email-news-digest",
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