A professional execution framework for Openclaw Skills that empowers AI agents with high autonomy, structured memory management, and proactive status reporting.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-work-protocol
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 openclaw-work-protocol using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The OpenClaw Working Protocol is a robust execution methodology designed specifically for Openclaw Skills to transform how AI agents interact with tasks and users. Built upon a Two-Lane Pipeline architecture, it separates core instructions from experimental data to prevent instruction drift and maintain high operational integrity. This protocol ensures that agents are not merely reactive but are self-driven entities capable of managing their own task lists and solving complex problems autonomously.
By incorporating Anthropic execution principles, the protocol enforces a rigorous standard for incremental progress and active communication. It provides a standardized way for agents to handle memory persistence, error analysis, and daily cleanup, making it an essential standard for anyone building advanced autonomous systems with Openclaw Skills.
To deploy this protocol within your Openclaw Skills environment, initialize the required memory structure in your workspace root:
# Create the essential memory and log files
touch MEMORY.md learning-log.md
# Create the directory for daily granular logs
mkdir -p memory/
Ensure your agent is configured to read from AGENTS.md for instructions and write to the Data Lane files for state persistence.
The OpenClaw Working Protocol utilizes a structured data hierarchy to ensure no information is lost across sessions. This is a core component of professional Openclaw Skills implementations.
| Component | File Path | Description |
|---|---|---|
| Long-term Memory | MEMORY.md |
Stores high-level project status and critical persistent knowledge. |
| Learning Log | learning-log.md |
Tracks experimental results, error analysis, and technical insights. |
| Daily Journal | memory/YYYY-MM-DD.md |
Granular record of all actions performed during a specific date. |
| Instruction Lane | AGENTS.md |
Contains the static core directives and task protocols for the agent. |
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