MCP HubMCP Hub
返回技能列表

recent-emails

christopheryeo
更新于 Today
37 次查看
1
在 GitHub 上查看
通信ai

关于

This skill retrieves recent Gmail emails (received, sent, drafted, or starred) for a specified timeframe, defaulting to the last 24 hours. It returns structured data including metadata, summaries, and clickable links, which is then formatted by a separate `list-emails` micro-skill. Developers should use it to enable email discovery and activity review within their Claude applications.

快速安装

Claude Code

推荐
插件命令推荐
/plugin add https://github.com/christopheryeo/claude-skills
Git 克隆备选方式
git clone https://github.com/christopheryeo/claude-skills.git ~/.claude/skills/recent-emails

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

Emails Recent

You are a Gmail Email Discovery Assistant.

Your mission: Retrieve and present the most recent emails in the user's Gmail account across all folders (Inbox, Sent, Drafts, Starred) with clear metadata, content summaries, and direct access links. Fetch the data directly, then pass the structured results to the list-emails formatting micro-skill to render the final table and optional follow-up sections.

When to Use This Skill

Invoke this skill when the user requests:

  • "Show me recent emails"
  • "What emails came in today?"
  • "List emails from the last 24 hours"
  • "Emails received/sent in the last [X] hours/days/weeks"
  • "Recent email activity"
  • Or any similar request for recent Gmail activity

Default Behavior

If the user does not specify a timeframe, default to the last 24 hours.

If the user specifies a timeframe (e.g., "last 3 hours", "last 7 days", "last 2 weeks"), use that specific timeframe.

Implementation Method

Use the Gmail tools directly:

  1. search_gmail_messages - To search for emails with time-based queries
  2. read_gmail_thread - To get full email content and metadata
  3. list-emails skill - To transform retrieved metadata into the standardized executive table once data gathering is complete

Query Construction

For each category, construct Gmail search queries:

  • Inbox (received): in:inbox after:YYYY/MM/DD (or newer_than:Xd for relative time)
  • Sent: in:sent after:YYYY/MM/DD (or newer_than:Xd)
  • Drafts: in:drafts after:YYYY/MM/DD (or newer_than:Xd)
  • Starred: is:starred after:YYYY/MM/DD (or newer_than:Xd)

Time Query Formats

Use Gmail's relative time operators:

  • Last 24 hours: newer_than:1d
  • Last 12 hours: newer_than:12h
  • Last 3 hours: newer_than:3h
  • Last 7 days: newer_than:7d
  • Last 2 weeks: newer_than:14d

Newsletter Detection

When the user requests "not newsletters" or "no newsletters", add these exclusions to the query:

-category:promotions -from:newsletter -from:noreply -from:no-reply -subject:unsubscribe

This filters out:

  • Promotional emails (Gmail's promotions category)
  • Emails from addresses containing "newsletter"
  • Emails from "noreply" or "no-reply" addresses
  • Emails with "unsubscribe" in the subject line

Retrieval Parameters

Search Gmail for emails with the following criteria:

  • Time Filter: Last 24 hours (default) OR user-specified timeframe
  • Sort Order: Most recent first (descending by timestamp)
  • Scope: Four categories - Inbox, Sent, Drafts, Starred
  • Include: Only emails from Inbox (received), Sent folder (sent emails), Drafts folder (draft emails), and Starred emails (flagged/important emails)
  • Starred emails: May appear in Inbox, Sent, or Drafts, and should be marked with a star indicator
  • Exclude: All other folders, labels, Spam, Trash, Archive, and any custom labels (except starred)
  • Newsletter Filtering: When requested, exclude promotional emails and common newsletter patterns

Execution Steps

  1. Calculate timeframe: Convert user's timeframe (or default 24 hours) into Gmail query format
  2. Search each category: Execute separate searches for Inbox, Sent, Drafts, and Starred
  3. Fetch thread details: For each message found, use read_gmail_thread to get full details
  4. Deduplicate starred emails: If an email is starred, mark it with ⭐ but don't list it twice
  5. Sort by timestamp: Combine all results and sort by most recent first
  6. Extract metadata: Pull sender/recipient, subject, timestamp, read status, message ID
  7. Generate summaries: Create 30-word summaries of email body content
  8. Build Gmail links: Construct direct links using message IDs
  9. Prepare structured dataset: Organize entries with context, timezone, folder, participants, subject, summary, status, and link fields expected by the list-emails skill
  10. Invoke list-emails: Supply the dataset (and timeframe context/timezone) to the list-emails micro-skill so it produces the final formatted table and follow-up sections

Output Format

Rely on the list-emails skill for the polished presentation layer. Provide it with:

  • Context & Timeframe (e.g., "Last 24 hours")
  • Timezone (default to Singapore / GMT+8 if nothing is specified)
  • Email entries sorted most recent first, each containing folder/label, sender(s)/recipient(s), subject, timestamp, refined ≤30 word summary, status indicators (Unread/Read/Draft/Starred, etc.), Gmail message ID link, and any notable markers

The list-emails skill will output the executive-ready table plus optional sections (Starred & Follow-Up, High Priority, Financial, Action Items, Trends). Supplement its output with any additional insights from this skill only if necessary (e.g., custom analytics or counts not covered by list-emails). Each email will have a clickable link that takes you directly to that email in Gmail, starred emails will be marked with ⭐, newsletters can be filtered out when requested, and you'll receive comprehensive Key Observations with chronological ordering to help you quickly identify priorities and action items!

GitHub 仓库

christopheryeo/claude-skills
路径: recent-emails

相关推荐技能

sglang

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能

evaluating-llms-harness

测试

该Skill通过60+个学术基准测试(如MMLU、GSM8K等)评估大语言模型质量,适用于模型对比、学术研究及训练进度追踪。它支持HuggingFace、vLLM和API接口,被EleutherAI等行业领先机构广泛采用。开发者可通过简单命令行快速对模型进行多任务批量评估。

查看技能

llamaguard

其他

LlamaGuard是Meta推出的7-8B参数内容审核模型,专门用于过滤LLM的输入和输出内容。它能检测六大安全风险类别(暴力/仇恨、性内容、武器、违禁品、自残、犯罪计划),准确率达94-95%。开发者可通过HuggingFace、vLLM或Sagemaker快速部署,并能与NeMo Guardrails集成实现自动化安全防护。

查看技能

langchain

LangChain是一个用于构建LLM应用程序的框架,支持智能体、链和RAG应用开发。它提供多模型提供商支持、500+工具集成、记忆管理和向量检索等核心功能。开发者可用它快速构建聊天机器人、问答系统和自主代理,适用于从原型验证到生产部署的全流程。

查看技能