A comprehensive framework for deploying and extending OpenClaw, a local-first, model-agnostic AI assistant platform built for privacy and autonomy.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pkuhb-openclaw-skill
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 pkuhb-openclaw-skill 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 OpenClaw Mastery & Skills Guide?
OpenClaw is a self-hosted AI assistant platform that prioritizes local data storage and user control, following a local-first philosophy. It operates as a model-agnostic gateway, allowing developers to integrate diverse LLMs like Claude, GPT, and DeepSeek while maintaining a unified execution layer.
By leveraging Openclaw Skills, users can extend the platform's functionality through a modular plugin architecture. The platform is distinguished by its unique file-based memory system and multi-channel integration, supporting communication through platforms like Feishu, WeChat, and Telegram. This guide, based on the Beijing University Xiao Rui team curriculum, provides the technical foundation for orchestrating sophisticated AI agents within the OpenClaw ecosystem.
OpenClaw Mastery & Skills Guide Use Cases
- Building privacy-centric AI assistants that store all conversational data and logic on local hardware.
- Developing custom automation workflows by creating new Openclaw Skills using Python, JavaScript, or Shell scripts.
- Implementing long-term memory for AI agents using a structured Markdown-based memory hierarchy.
- Deploying multi-agent systems that can collaborate on complex planning and execution tasks.
How OpenClaw Mastery & Skills Guide Works
- The platform receives user input through a configured communication channel (e.g., CLI, IM app).
- An intent recognition phase identifies the user's core request.
- The system performs context retrieval using a hybrid RAG approach, combining vector search with BM25 keyword matching.
- A task planning phase breaks the request into smaller steps based on the agent's defined SOUL.md principles.
- The agent invokes relevant Openclaw Skills to perform specific actions or data processing.
- The agent observes the results of the tool execution and updates its state.
- A final decision is made, and a response is sent back to the user via the original channel.
OpenClaw Mastery & Skills Guide Setup
To set up the environment for Openclaw Skills, follow these steps:
# Create the standard workspace directory
mkdir -p ~/.openclaw/workspace/skills
# Initialize core persona and user profile files
touch ~/.openclaw/workspace/SOUL.md
touch ~/.openclaw/workspace/USER.md
# After adding a new skill to the skills directory, restart the gateway
openclaw gateway restart
OpenClaw Mastery & Skills Guide Data Schema & Taxonomy
OpenClaw utilizes a file-based taxonomy to organize agent intelligence and memory:
| File/Directory | Role | Metadata Tracked |
|---|---|---|
| SOUL.md | Persona | Core principles, tone, and behavioral redlines. |
| USER.md | Profile | User preferences, habits, and constraints. |
| MEMORY.md | Long-term | High-level summaries of past interactions. |
| memory/ | Logs | Daily session transcripts (YYYY-MM-DD.md). |
| skills/ | Tools | Individual folders containing SKILL.md and execution logic. |
OpenClaw Mastery & Skills Guide Advanced Features
- Hybrid Retrieval Augmented Generation (RAG) with time-decay weighting to prioritize recent memories.
- Multi-model strategy support, allowing different LLMs for simple vs. complex planning tasks.
- Autonomous 'Heartbeat' tasks for scheduled agent activities and self-initiated workflows.
- Modular Openclaw Skills development framework with strict security auditing and permission sandboxing.
- Multi-agent collaboration protocols defined via AGENTS.md for complex team-based automation.
SKILL.md
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