openclaw-gen

What This Skill Does

The openclaw-gen skill serves as a high-efficiency configuration engine designed specifically for the OpenClaw ecosystem. By leveraging the unified llm-task interface, it removes the complexity of manually drafting project files. This tool acts as an intelligent generator that fabricates the essential skeleton of any OpenClaw project, specifically producing AGENTS.md, workflows/ directory, and MEMORY.md. With 15 pre-configured templates, it allows developers to bootstrap diverse agents—ranging from personal productivity tools like daily-assistants to technical utilities like DevOps monitors—in seconds rather than hours. It operates entirely within the agent's internal logic, ensuring seamless integration without the need for additional API keys or external environment configurations.

Installation

To integrate this skill into your environment, ensure you have the OpenClaw CLI installed and initialized. You can install the generator by running the following command in your terminal:

clawhub install openclaw/skills/skills/truongvknnlthao-gif/openclaw-template-generator

Once installed, verify that your ~/.openclaw/openclaw.json configuration has the llm-task plugin enabled. Simply navigate to your projects folder and trigger the tool through the OpenClaw agent interface to begin generating scaffolding.

Use Cases

This skill is perfect for developers who frequently spin up new agents for varying tasks. Use cases include:

• Rapid Prototyping: Quickly testing how different persona definitions impact agent behavior.
• Structured Workflow Creation: Automatically generating yaml-based workflows that comply with OpenClaw standards.
• Standardized Onboarding: Creating consistent memory and agent configurations across team projects to ensure maintainability.
• Automation Pipelines: Setting up repetitive bots like social media managers or research assistants without writing configuration boilerplate from scratch.

Example Prompts

1. "I need a research assistant to help me summarize deep learning papers. Use the research-assistant template to generate the necessary files."
2. "Create a new project structure for a finance-tracker that monitors my daily expenses and sends a summary report."
3. "Setup a github-monitor agent that watches my repositories for new issues and notifies me via a workflow."

Tips & Limitations

• Tips: Always review the generated AGENTS.md after generation to fine-tune the specific behavioral prompt for your unique model. Utilize the provided template categories to minimize the prompt complexity required for your request.
• Limitations: The skill relies on the llm-task interface; therefore, your configuration must correctly point to a supported model like MiniMax-M2.1 for optimal results. It is designed to generate project structure and basic logic, not to write complex, production-ready backend code from scratch.