A sophisticated multi-agent orchestration system that organizes AI models into specialized professional teams including software development, business strategy, and creative writing.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install team-collab
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install team-collab using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Team Collab is a high-performance framework designed for Openclaw Skills that transforms standalone AI models into cohesive, professional business units. By utilizing a fixed sub-window architecture, it assigns specific roles to the most capable models—such as qwen3.5-plus for creativity and glm-5 for logic—ensuring each task is handled by an expert agent with persistent memory.
This skill enables users to operate like a full-scale company, providing structured workflows for complex projects. Whether you are building software, launching a commercial venture, or drafting a web novel, Team Collab coordinates up to 28 specialized AI roles to deliver professional-grade results through a unified collaboration protocol.
To integrate this skill into your environment, follow the standard Openclaw Skills installation process:
# Install the team-collab skill
openclaw install team-collab
# Configure model API keys for qwen3.5-plus and glm-5 in your .env
# Start the multi-agent environment
openclaw run team-collab
Team Collab uses a standardized naming convention for its sub-window architecture to maintain state and organizational clarity:
| Team Prefix | Role Key | Persistent Label |
|---|---|---|
| dev- | pm, coder, test, review | dev-[role] |
| money- | hunter, advisor, tech | money-[role] |
| novel- | expert, world, plot | novel-[role] |
All session data and generated artifacts are indexed by these labels, allowing for multi-agent memory persistence across different conversation segments.
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