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Agent Skill · 开发

listener-creator(听众创造者)

listener-creator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

listener-creator 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 11,670。

总安装 1,640周安装 67GitHub Stars 2,313下载 531核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:listener-creator(听众创造者)
来源仓库:https://github.com/anthropics/claude-agent-sdk-demos
仓库路径:skills/listener-creator
安装命令:
npx skills add https://github.com/anthropics/claude-agent-sdk-demos --skill listener-creator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

listener-creator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态或协作事项进行整理。通过 npx 命令从指定 GitHub 仓库安装并使用。

使用前需确认权限范围、维护状态及是否涉及联网或文件操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/listener-creator

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/anthropics/claude-agent-sdk-demos --skill list…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

listener-creator 怎么安装?

在终端执行 npx skills add https://github.com/anthropics/claude-agent-sdk-demos --skill listener-creat…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

listener-creator 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

listener-creator 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

listener-creator 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

listener-creator 用的人多吗?

公开数据是累计安装 1,640 次,近一周安装 67 次,下载 531 次,GitHub 2,313 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

listener-creator 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 2,313 星,维护活跃度相对可查。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
listener-creator

听众创造者 · 本页

1,6402,313只读未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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:

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?

  1. Choose the right event type (usually email_received)
  2. Write the TypeScript file in agent/custom_scripts/listeners/
  3. Use Write tool to create the file with:

- Proper imports - Descriptive config - Handler with early filtering - Appropriate context method calls

  1. 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
  2. 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

平台分布

Codex

35.92%
按下载量换算191

Claude

29.89%
按下载量换算159

Cursor

19.04%
按下载量换算101

Gemini CLI

8.21%
按下载量换算44

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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