MCPInstall

listener-creator

Creates event-driven email listeners that monitor for specific conditions (like urgent emails from boss, newsletters to archive, package tracking) and execute custom actions. Use when user wants to be notified about emails, automatically handle certain emails, or set up email automation workflows.

Installs
165
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/anthropics/claude-agent-sdk-demos --skill listener-creator
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads listener-creator automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Listener Creator

Creates TypeScript listener files that monitor email events and execute custom logic when conditions are met.

When to Use This Skill

Use this skill when the user wants to:

  • Get notifications about specific emails ("notify me when boss sends urgent emails")
  • Automatically handle certain emails ("auto-archive newsletters")
  • Monitor for patterns ("watch for package tracking emails")
  • Set up scheduled actions ("daily email summary at 9am")
  • Create custom email workflows

How Listeners Work

Listeners are TypeScript files in agent/custom_scripts/listeners/ that:

  1. Export a config object defining the event type and metadata
  2. Export a handler function that filters and processes events
  3. Use ListenerContext methods to perform actions (notify, archive, star, etc.)

The system automatically loads enabled listeners and executes them when matching events occur.

Creating a Listener

1. Understand the User's Intent

Parse the user's request to identify:

  • Event type: What triggers this listener? (email_received, email_sent, email_starred, email_archived, email_labeled, scheduled_time)
  • Filter conditions: What specific emails/events to match? (sender, subject keywords, time-based)
  • Actions: What should happen? (notify, archive, star, mark as read, add label)
  • Priority: How urgent is this? (high/normal/low)

2. Choose an Event Type

// Available event types:
- "email_received"  // Most common - new email arrives
- "email_sent"      // User sends an email
- "email_starred"   // Email is starred
- "email_archived"  // Email is archived
- "email_labeled"   // Label added to email
- "scheduled_time"  // Time-based (cron) - requires scheduler setup

3. Write the Listener File

Create a file in agent/custom_scripts/listeners/ with this structure:

import type { ListenerConfig, Email, ListenerContext } from "../types";

export const config: ListenerConfig = {
  id: "unique_listener_id",           // kebab-case, descriptive
  name: "Human Readable Name",         // For UI display
  description: "What this does",       // Optional but helpful
  enabled: true,                       // Start enabled
  event: "email_received"              // Event type
};

export async function handler(email: Email, context: ListenerContext): Promise<void> {
  // 1. Basic filter (identity/sender only)
  if (!email.from.includes("example@email.com")) return;

  // 2. Use AI for intelligent classification (PREFERRED over keyword matching)
  const analysis = await context.callAgent<{ isUrgent: boolean; reason: string }>({
    prompt: `Is this email urgent?\nSubject: ${email.subject}\nBody: ${email.body.substring(0, 500)}`,
    schema: {
      type: "object",
      properties: {
        isUrgent: { type: "boolean" },
        reason: { type: "string" }
      },
      required: ["isUrgent", "reason"]
    },
    model: "haiku"
  });

  if (!analysis.isUrgent) return;

  // 3. Perform actions via context methods
  await context.notify(`Urgent email: ${email.subject}\n${analysis.reason}`, {
    priority: "high"
  });

  await context.starEmail(email.messageId);
}

4. File Naming Convention

Use kebab-case matching the listener's purpose:

  • boss-urgent-watcher.ts
  • auto-archive-newsletters.ts
  • package-tracking.ts
  • daily-summary.ts

5. Available Context Methods

The ListenerContext provides these methods:

// Notifications
await context.notify(message, { priority: "high" | "normal" | "low" });

// Email actions
await context.archiveEmail(emailId);
await context.starEmail(emailId);
await context.unstarEmail(emailId);
await context.markAsRead(emailId);
await context.markAsUnread(emailId);
await context.addLabel(emailId, "label-name");
await context.removeLabel(emailId, "label-name");

// AI-powered analysis
const result = await context.callAgent<ResultType>({
  prompt: "Your prompt with email content",
  schema: {
    type: "object",
    properties: { field: { type: "string" } },
    required: ["field"]
  },
  model: "haiku" // or "sonnet" or "opus"
});

Recommended Approach: AI-Powered Classification

Default to using context.callAgent() for intelligent decision-making instead of hard-coded keyword lists. This provides better accuracy and adaptability.

// PREFERRED: AI-based urgency detection
const analysis = await context.callAgent<{ isUrgent: boolean; reason: string }>({
  prompt: `Analyze if this email is urgent:
Subject: ${email.subject}
Body: ${email.body.substring(0, 500)}

Is this email urgent or time-sensitive? Consider context, not just keywords.`,

  schema: {
    type: "object",
    properties: {
      isUrgent: { type: "boolean" },
      reason: { type: "string" }
    },
    required: ["isUrgent", "reason"]
  },
  model: "haiku" // Fast and cost-effective
});

if (analysis.isUrgent) {
  await context.notify(`Urgent: ${email.subject}\n${analysis.reason}`);
}

// AVOID: Hard-coded keyword lists (brittle and prone to false positives)
// const isUrgent = subject.includes("urgent") || subject.includes("asap");

Examples and Templates

Reference the template files for common patterns:

  • ai-classifier.ts: ⭐ AI-powered email classification (RECOMMENDED)
  • urgent-watcher.ts: AI-based urgent email detection
  • auto-archive.ts: Automatically archives newsletters
  • package-tracker.ts: Package tracking with status extraction

Best Practices

  1. Prefer AI Classification: Use context.callAgent() instead of hard-coded keyword lists for intelligent decision-making
  2. Filter Early: Return early if email doesn't match basic criteria (like sender)
  3. Clear IDs: Use descriptive, unique listener IDs
  4. Error Handling: Wrap context method calls in try-catch when appropriate
  5. Performance: Use "haiku" model for fast AI classification (< 1 second typical)
  6. Notify Wisely: Only notify when truly important
  7. Avoid Hard-Coded Lists: Let AI determine urgency, importance, or categories instead of keyword matching

Type Imports

Always import types from the correct location:

import type { ListenerConfig, Email, ListenerContext } from "../types";

// For scheduled listeners:
import type { ListenerConfig, ListenerContext } from "../types";

// For labeled event:
import type { ListenerConfig, Email, ListenerContext } from "../types";

Common Patterns

AI-Powered (PREFERRED)

Basic filter (sender/type) → Call AI agent for intelligent classification → Act on AI result → Notify if important

This is the recommended approach for most listeners as it:

  • Avoids brittle keyword matching
  • Adapts to nuanced language and context
  • Makes better decisions about urgency and categorization
  • Reduces false positives

Simple Notification (Use sparingly)

Basic filter (sender only) → Notify → Optional star/label

Only use this when: The trigger is purely identity-based (e.g., "notify me about ALL emails from X")

Auto-Archive

Basic filter → Archive → Mark as read → Optional notify

Scheduled

Run at specific time → Query emails → Analyze → Send summary

Creating the File

When the user requests a listener:

  1. Ask clarifying questions if the intent is unclear:

    • Who is the sender? What keywords?
    • What action should happen?
    • How urgent is this?
  2. Choose the right event type (usually email_received)

  3. Write the TypeScript file in agent/custom_scripts/listeners/

  4. Use Write tool to create the file with:

    • Proper imports
    • Descriptive config
    • Handler with early filtering
    • Appropriate context method calls
  5. Return listener reference in markdown format using [listener:filename.ts] notation (e.g., [listener:boss-urgent-watcher.ts]) for easy parsing and linking in the UI

  6. Confirm with user that the listener matches their intent

Output Format Example

When presenting a created listener to the user, use this format:

Created listener: [listener:boss-urgent-watcher.ts]

This listener will:
- Monitor emails from boss@company.com
- Use AI to detect urgent emails (not just keywords)
- Send high-priority notifications for truly urgent emails
- Star emails that require immediate action

When to Use AI vs Simple Filtering

Use AI (context.callAgent()) when:

  • Detecting urgency, importance, or sentiment
  • Classifying email content or intent
  • Extracting structured data from email bodies
  • Making nuanced decisions based on context
  • Any logic that involves "understanding" the email content

Use simple filtering when:

  • Checking exact sender/recipient
  • Basic pattern matching on email fields (e.g., "from specific domain")
  • Identity-based triggers (e.g., "all emails from X person")

Default to AI unless the filter is purely identity-based.

Scheduled Listeners

For time-based actions (daily summaries, weekly reports):

export const config: ListenerConfig = {
  id: "daily_summary",
  name: "Daily Email Summary",
  enabled: true,
  event: "scheduled_time"
  // Note: Cron schedule configured separately in scheduler
};

export async function handler(
  data: { timestamp: Date },
  context: ListenerContext
): Promise<void> {
  // Your scheduled logic here
  await context.notify("Good morning! Your daily summary...");
}

Note: Scheduled listeners require cron scheduler configuration outside the listener file.

Reference

Full specification: See project root LISTENERS_SPEC.md for complete details on:

  • All event types
  • Complete type definitions
  • ListenersManager implementation
  • Advanced examples
  • Error handling patterns

FAQ

What does the listener-creator skill do?

Creates event-driven email listeners that monitor for specific conditions (like urgent emails from boss, newsletters to archive, package tracking) and execute custom actions. Use when user wants to be notified about emails, automatically handle certain emails, or set up email automation workflows.

How do I install listener-creator?

Run npx skills add https://github.com/anthropics/claude-agent-sdk-demos --skill listener-creator in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/listener-creator/ (personal) or .claude/skills/listener-creator/ (project).

Which AI agents can use listener-creator?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is listener-creator safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains listener-creator?

It is published by anthropics in anthropics/claude-agent-sdk-demos, and it is an official skill from the maker of the technology it covers. It has 165 recorded installs.

More from anthropics/claude-agent-sdk-demos

View all →

executive-briefing

anthropics/claude-agent-sdk-demos

Open the skill to read its SKILL.md instructions.

Official341 installs

docx

anthropics/claude-agent-sdk-demos

Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks

OfficialDocs & Writing219 installs

pdf

anthropics/claude-agent-sdk-demos

Open the skill to read its SKILL.md instructions.

OfficialDocs & Writing198 installs

xlsx

anthropics/claude-agent-sdk-demos

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

OfficialData & Databases179 installs

action-creator

anthropics/claude-agent-sdk-demos

Open the skill to read its SKILL.md instructions.

Official173 installs

Popular productivity & automation skills

View category →

agent-browser

vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

OfficialProductivity & Automation1.3M installs

lark-doc

open.feishu.cn

飞书云文档(Docx / Wiki)内容操作:读取、创建、编辑文档,插入或下载图片附件,以及操作思维笔记。用户提供文档 URL/token(包括 doubao.com 的 /docx/、/wiki/)时使用;按 URL 路径/token 而非域名路由;/page/ 是妙搭应用,非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive;随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。

lark-base

open.feishu.cn

飞书多维表格(Base)操作:建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式(BaseApp/AppMode 页面与组件)、Workspace 目录、workflow、角色权限、模板中心(多维表格模板分类/列表/搜索);遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps;文件导入/导出转 lark-drive,认证/授权转 lark-shared。

lark-drive

open.feishu.cn

飞书云空间(云盘/云存储):管理 Drive 文件和文件夹,包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签(secure labels)和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题,或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用;doubao.com 云空间 URL/token 也按资源路径和 token 路由,不回退 WebFetch。不负责:文档内容编辑(走 lark-doc)、表格/Base 表内数据操作(走 lark-sheets/lark-base)、知识空间节点/成员管理(走 lark-wiki)、原生 Markdown 文件读写/patch/diff(走 lark-markdown)。

lark-shared

open.feishu.cn

Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

lark-im

open.feishu.cn

飞书即时通讯:收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片(Interactive Card)、监听卡片按钮回调(card.action.trigger)。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶(添加/移除/查询置顶会话)、管理标签数据、处理卡片回调时使用。