elevenlabs-agents

jezweb/claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jezweb/claude-skills --skill elevenlabs-agents
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summary

Build production-ready conversational AI voice agents with ElevenLabs Platform.

  • Supports React, React Native, Swift, and JavaScript SDKs with dashboard or CLI-based agent configuration
  • Add client-side and webhook-based server tools, upload knowledge bases for RAG, and configure voice, LLM, system prompt, and first message
  • Includes signed URL authentication pattern, agent versioning for A/B testing, and dynamic variable injection for user context
  • CLI provides init, deploy, test, an
skill.md

ElevenLabs Agent Builder

Build a production-ready conversational AI voice agent. Produces a configured agent with tools, knowledge base, and SDK integration.

Packages

npm install @elevenlabs/react           # React SDK
npm install @elevenlabs/client          # JavaScript SDK (browser + server)
npm install @elevenlabs/react-native    # React Native SDK
npm install @elevenlabs/elevenlabs-js   # Full API (server only)
npm install -g @elevenlabs/agents-cli   # CLI ("Agents as Code")

DEPRECATED: @11labs/react, @11labs/client -- uninstall if present.

Server-only warning: @elevenlabs/elevenlabs-js uses Node.js child_process and won't work in browsers. Use @elevenlabs/client for browser environments, or create a proxy server.

Workflow

Step 1: Create Agent via Dashboard or CLI

Dashboard: https://elevenlabs.io/app/conversational-ai -> Create Agent

CLI (Agents as Code):

elevenlabs agents init
elevenlabs agents add "Support Bot" --template customer-service
# Edit agent_configs/support-bot.json
elevenlabs agents push --env dev

Templates: default, minimal, voice-only, text-only, customer-service, assistant.

Configure:

  • Voice -- Choose from 5000+ voices or clone
  • LLM -- GPT, Claude, Gemini, or custom
  • System prompt -- Use the 6-component framework below
  • First message -- What the agent says when conversation starts

Step 2: Write the System Prompt

Use the 6-component framework for effective agent prompts:

1. Personality -- who the agent is:

You are [NAME], a [ROLE] at [COMPANY].
You have [EXPERIENCE]. Your traits: [LIST TRAITS].

2. Environment -- communication context:

You're communicating via [phone/chat/video].
Consider [environmental factors]. Adapt to [context].

3. Tone -- speech patterns and formality:

Tone: Professional yet warm. Use contractions for natural speech.
Avoid jargon. Keep responses to 2-3 sentences. Ask one question at a time.

4. Goal -- objectives and success criteria:

Primary Goal: Resolve customer issues on the first call.
Success: Customer verbally confirms issue is resolved.

5. Guardrails -- boundaries and ethics:

Never: provide medical/legal/financial advice, share confidential info.
Always: verify identity before account access, document interactions.
Escalation: customer requests manager, issue beyond knowledge base.

6. Tools -- available functions and when to use them:

1. lookup_order(order_id) -- Use when customer mentions an order.
2. transfer_to_supervisor() -- Use when issue requires manager approval.
Always explain what you're doing before calling a tool.

Step 3: Add Tools

Client-side tools (run in browser):

const clientTools = {
  updateCart: {
    description: "Add or remove items from the shopping cart",
    parameters: z.object({
      action: z.enum(['add', 'remove']),
      item: z.string(),
      quantity: z.number().min(1)
    }),
    handler: async ({ action, item, quantity }) => {
      const cart = getCart();
      action === 'add' ? cart.add(item, quantity) : cart.remove(item, quantity);
      return { success: true, total: cart.total, items: cart.items.length };
    }
  },
  navigate: {
    description: "Navigate user to a different page",
    parameters: z.object({ url: z.string().url() }),
    handler: async ({ url }) => { window.location.href = url; return { success: true }; }
  }
};

Server-side tools (webhooks):

{
  "name": "get_weather",
  "description": "Fetch current weather for a city",
  "url": "https://api.weather.com/v1/current",
  "method": "GET",
  "parameters": {
    "type": "object",
    "properties": {
      "city": { "type": "string", "description": "City name" }
    },
    "required": ["city"]
  },
  "headers": {
    "Authorization": "Bearer {{secret__weather_api_key}}"
  }
}

Use {{secret__key_name}} for API keys in webhook headers -- never hardcode.

MCP Tools -- CRITICAL COMPATIBILITY NOTE:

ElevenLabs labels their MCP integration as "Streamable HTTP" but does NOT support the actual MCP 2025-03-26 Streamable HTTP spec (SSE responses). ElevenLabs expects:

  • Plain JSON responses (application/json), NOT SSE (text/event-stream)
  • Protocol version 2024-11-05, NOT 2025-03-26
  • Simple JSON-RPC over HTTP with direct JSON responses

What does NOT work:

  • Official MCP SDK's createMcpHandler (returns SSE)
  • Cloudflare Agents SDK McpServer.serve() (returns SSE)
  • Any server returning Content-Type: text/event-stream

Working MCP server pattern for ElevenLabs:

import { Hono } from 'hono';
import { cors } from 'hono/cors';

const tools = [{
  name: "my_tool",
  description: "Tool description",
  inputSchema: {
    type: "object",
    properties: { param1: { type: "string", description: "Description" } },
    required: ["param1"]
  }
}];

async function handleMCPRequest(request, env) {
  const { id, method, params } = request;
  switch (method) {
    case 'initialize':
      return {
        jsonrpc: '2.0', id,
        result: {
          protocolVersion: '2024-11-05',  // MUST be 2024-11-05
          serverInfo: { name: 'my-mcp', version: '1.0.0' },
          capabilities: { tools: {} }
        }
      };
    case 'tools/list':
      return { jsonrpc: '2.0', id, result: { tools } };
    case 'tools/call':
      const result = await handleTool(params.name, params.arguments, env);
      return { jsonrpc: '2.0', id, result };
    default:
      return { jsonrpc: '2.0', id, error: { code: -32601, message: `Unknown: ${method}` } }
how to use elevenlabs-agents

How to use elevenlabs-agents on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add elevenlabs-agents
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jezweb/claude-skills --skill elevenlabs-agents

The skills CLI fetches elevenlabs-agents from GitHub repository jezweb/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/elevenlabs-agents

Reload or restart Cursor to activate elevenlabs-agents. Access the skill through slash commands (e.g., /elevenlabs-agents) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.868 reviews
  • Arya Okafor· Dec 28, 2024

    I recommend elevenlabs-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Pratham Ware· Dec 20, 2024

    elevenlabs-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chinedu Abbas· Dec 12, 2024

    elevenlabs-agents reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Dev Reddy· Dec 8, 2024

    elevenlabs-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ama Mensah· Dec 8, 2024

    We added elevenlabs-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Arya Bansal· Dec 4, 2024

    Keeps context tight: elevenlabs-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Arjun Flores· Dec 4, 2024

    Solid pick for teams standardizing on skills: elevenlabs-agents is focused, and the summary matches what you get after install.

  • Tariq Dixit· Nov 27, 2024

    Keeps context tight: elevenlabs-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Luis Thomas· Nov 23, 2024

    We added elevenlabs-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Emma Farah· Nov 23, 2024

    elevenlabs-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.

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