How do I install the Pipecat skill?

Run npx skillmds@latest add akillness/pipecat in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Pipecat skill do?

Agent engineering skill for Pipecat It is listed under AI & ML on SkillMD.

Is Pipecat safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Pipecat?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pipecat free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under BSD-2-Clause.

Who published Pipecat?

akillness (@akillness) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pipecat by akillness — Agent Skill

Name: Pipecat
Author: akillness

File contents

pipecat

Pipecat is a leading voice agent in the 2026 AI agent engineering landscape (13.1k★).

When to use this skill

Frame/processor/pipeline architecture for realtime voice + multimodal agents
40+ service plugins: STT/TTS/LLM providers, transports (Daily WebRTC, WebSocket, telephony)
Pipecat Flows for structured conversation state machines
Client SDKs: JS, React, iOS, Android + voice UI kit
Vendor-neutral: swap any provider per pipeline stage
Used by NVIDIA ACE and Daily production stacks
Ecosystem overlap: Pairs with livekit-agents as the two OSS voice lanes

Installation

uv add pipecat-ai   # extras: "pipecat-ai[option,...]"; pip also works

Core API

pipeline = Pipeline([
    transport.input(),
    stt,            # e.g. Deepgram
    context_aggregator.user(),
    llm,            # e.g. OpenAI
    tts,            # e.g. Cartesia
    transport.output(),
    context_aggregator.assistant(),
])
task = PipelineTask(pipeline)
await PipelineRunner().run(task)

Instructions

Step 1: Initialize the environment

Verify you have Python installed and set up properly. Use the correct package manager (pip, npm, pnpm, uv) as specified in the installation section.

Step 2: Establish the agent flow

Architecture pattern: Dataflow pipeline: frames (audio/text/LLM messages) stream through processors; ultra-low-latency scheduling; transports abstract the realtime channel

Step 3: Implement tool integrations and policies

Integrate necessary tools, environment keys, and safety gates according to your architectural needs. Position in the ecosystem: Composable pipeline lane of voice agents vs LiveKit's infrastructure lane; strongest provider neutrality

Examples

Example 1: Minimal Agent Run

pipeline = Pipeline([
    transport.input(),
    stt,            # e.g. Deepgram
    context_aggregator.user(),
    llm,            # e.g. OpenAI
    tts,            # e.g. Cartesia
    transport.output(),
    context_aggregator.assistant(),
])
task = PipelineTask(pipeline)
await PipelineRunner().run(task)

Best practices

Follow the primary installation commands for Pipecat to ensure dependencies are resolved correctly.
Configure model providers and API keys securely before starting the runtime.
Use appropriate sandboxing or permission isolation if executing AI-generated code.

References

---name: pipecatdescription: Agent engineering skill for Pipecatlicense: BSD-2-Clause---# pipecatPipecat is a leading voice agent in the 2026 AI agent engineering landscape (13.1k★).## When to use this skill- Frame/processor/pipeline architecture for realtime voice + multimodal agents- 40+ service plugins: STT/TTS/LLM providers, transports (Daily WebRTC, WebSocket, telephony)- Pipecat Flows for structured conversation state machines- Client SDKs: JS, React, iOS, Android + voice UI kit- Vendor-neutral: swap any provider per pipeline stage- Used by NVIDIA ACE and Daily production stacks- Ecosystem overlap: Pairs with livekit-agents as the two OSS voice lanes## Installation```bashuv add pipecat-ai # extras: "pipecat-ai[option,...]"; pip also works```## Core API```pythonpipeline = Pipeline([ transport.input(), stt, # e.g. Deepgram context_aggregator.user(), llm, # e.g. OpenAI tts, # e.g. Cartesia transport.output(), context_aggregator.assistant(),])task = PipelineTask(pipeline)await PipelineRunner().run(task)```## Instructions### Step 1: Initialize the environmentVerify you have Python installed and set up properly. Use the correct package manager (pip, npm, pnpm, uv) as specified in the installation section.### Step 2: Establish the agent flowArchitecture pattern: Dataflow pipeline: frames (audio/text/LLM messages) stream through processors; ultra-low-latency scheduling; transports abstract the realtime channel### Step 3: Implement tool integrations and policiesIntegrate necessary tools, environment keys, and safety gates according to your architectural needs. Position in the ecosystem: Composable pipeline lane of voice agents vs LiveKit's infrastructure lane; strongest provider neutrality## Examples### Example 1: Minimal Agent Run```pythonpipeline = Pipeline([ transport.input(), stt, # e.g. Deepgram context_aggregator.user(), llm, # e.g. OpenAI tts, # e.g. Cartesia transport.output(), context_aggregator.assistant(),])task = PipelineTask(pipeline)await PipelineRunner().run(task)```## Best practices1. Follow the primary installation commands for Pipecat to ensure dependencies are resolved correctly.2. Configure model providers and API keys securely before starting the runtime.3. Use appropriate sandboxing or permission isolation if executing AI-generated code.## References- [https://github.com/pipecat-ai/pipecat](https://github.com/pipecat-ai/pipecat)- [https://docs.pipecat.ai](https://docs.pipecat.ai)

Frequently asked questions

Pipecat