Fetches elevenlabs-agents from jezweb/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/elevenlabs-agents
Restart Cursor to activate elevenlabs-agents. Access via /elevenlabs-agents in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Build a production-ready conversational AI voice agent. Produces a configured agent with tools, knowledge base, and SDK integration.
Packages
npminstall @elevenlabs/react # React SDKnpminstall @elevenlabs/client # JavaScript SDK (browser + server)npminstall @elevenlabs/react-native # React Native SDKnpminstall @elevenlabs/elevenlabs-js # Full API (server only)npminstall-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.
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
›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
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share 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
1Basic: user stories, feature specs, status updates
