agent-autopilot

What This Skill Does

The agent-autopilot skill transforms an OpenClaw agent into a high-level autonomous project manager. By leveraging a heartbeat-driven execution model, the agent manages its own task lifecycle—from decomposition and prioritization to execution and periodic reporting. It ensures that projects move forward continuously without needing constant manual intervention, functioning similarly to a dedicated sub-agent that tracks work through the todo-management integration.

Installation

To install this skill, use the ClawHub command within your terminal:

clawhub install openclaw/skills/skills/edoserbia/agent-autopilot

After installation, initialize your specific agent workspace to set up the necessary directory structures, configuration files, and state trackers:

bash {baseDir}/scripts/init.sh <agent工作空间路径>

This setup process ensures all dependencies are met, including the integration with the todo-management suite.

Use Cases

• Autonomous Research Projects: Allow the agent to research a topic, break it down into incremental steps, and consolidate findings into a summary document periodically.
• Continuous Development Cycles: Enable an agent to manage its own coding tasks, testing, and debugging workflow using a backlog tracked by the todo-management system.
• Routine Maintenance Tasks: Schedule an agent to perform regular system checks or log maintenance, with automatic summaries provided at the end of each work day.

Example Prompts

1. "Initialize the agent-autopilot workspace for my current directory so you can start managing the project tasks autonomously."
2. "Monitor the 'website-redesign' task group and ensure you are pushing updates to the progress report whenever a major milestone is reached."
3. "Review the status of the current sprint; if all items are marked as 'in_progress' or 'done' but the project goal is unmet, break down the remaining work into new pending tasks."

Tips & Limitations

• Heartbeat Consistency: Ensure your environment allows for consistent heartbeat triggers (typically every 30 minutes) to maintain the agent's momentum.
• Prompt Engineering: The HEARTBEAT.md configuration is critical. Ensure your custom instructions for task decomposition are clear to prevent the agent from stalling when it encounters ambiguous goals.
• Memory Management: Because the agent consolidates long-term memory every 6 hours, it is essential to keep your working files organized to ensure the MEMORY.md file remains relevant and high-quality.
• Monitoring: While the agent is autonomous, you should review the memory/report-state.json file periodically to ensure the heartbeat cycles are firing correctly.