A comprehensive framework for hiring, training, and managing specialized virtual AI employees with persistent memory and graduated autonomy.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install employee
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install employee using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Employee AI Management Skill?
The Employee skill transforms your AI environment into a managed virtual workforce. It enables the creation of specialized AI agents, each with a single defined role—such as a researcher, reviewer, or support agent—ensuring focused expertise rather than generalist mediocrity. By utilizing this Openclaw Skills extension, users can implement a structured hierarchy where AI agents operate within explicit permission boundaries and escalation protocols, ensuring high-quality output and system safety.
Every employee created within this framework is designed to be a persistent entity. Unlike standard stateless prompts, these agents maintain their own memory files, work logs, and performance metrics. This allows for a more sophisticated delegation model where the AI learns from previous interactions and matures through defined autonomy levels, ranging from simple shadowing to fully autonomous operation.
Employee AI Management Skill Use Cases
- Automating code reviews with a dedicated Reviewer agent that identifies bugs without having merge permissions.
- Conducting deep technical research through a Researcher agent that maintains long-term context and specialized documentation.
- Handling repetitive customer support queries or documentation updates using a Support agent with whitelisted file access.
- Managing complex multi-step projects by delegating specific sub-tasks to a team of specialized AI workers defined in a central registry.
How Employee AI Management Skill Works
- The system initializes a central registry to track all virtual workers and their operational status across the Openclaw Skills environment.
- You hire a new employee by defining a clear domain, such as 'Backend Architect', and configuring their specific permissions in a JSON file.
- The agent is assigned an initial autonomy level, typically starting as a 'shadow' to observe workflows without taking independent action.
- Before every task, the agent loads its persistent context memory to ensure it has all relevant historical data and project-specific knowledge.
- Upon completion of a task, the agent logs its learnings and provides a report, allowing for performance evaluation and potential promotion to higher autonomy levels.
Employee AI Management Skill Setup
To set up the Employee framework within your Openclaw Skills workspace, create the necessary directory structure and initialize the registry:
mkdir -p ~/employee/employees
mkdir -p ~/employee/shared
touch ~/employee/registry.json
Once the structure is ready, you can hire your first agent using the lifecycle commands. Ensure you have a template file ready to define the agent's initial role and permission parameters.
Employee AI Management Skill Data Schema & Taxonomy
The Employee skill organizes data using a strict hierarchical structure to ensure context persistence and auditability:
| Component | Path | Purpose |
|---|---|---|
| Registry | ~/employee/registry.json |
The master index of all employees, roles, and current statuses. |
| Config | employees/{name}/employee.json |
Stores role definitions, permission whitelists, and performance stats. |
| Memory | employees/{name}/memory/context.md |
A persistent Markdown file containing long-term learnings and specific context. |
| Logs | employees/{name}/logs/ |
Historical records of work performed, organized by date. |
| Shared | shared/protocols.md |
Global instructions and standards that apply to all employees in the system. |
Employee AI Management Skill Advanced Features
- Graduated Autonomy: Systematically promote agents through levels including shadow, draft-only, review, and autonomous based on performance.
- Persistent Contextual Memory: Agents are required to load and update a context.md file, ensuring they never lose knowledge between tasks.
- Explicit Permission Whitelisting: Define exactly which file paths and system actions (like canSpawn or canMessage) an agent is allowed to access.
- Automated Reporting Cycles: Built-in support for daily and weekly reporting on tasks completed, escalations triggered, and token usage statistics.
- Conflict Resolution Routing: Advanced task routing that matches requests against the registry to ensure the right specialist handles the right request.
SKILL.md
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