A technical implementation that forces AI agents to treat automated messages as mandatory tasks rather than passive background information.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-prompt
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 self-prompt 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 Self-Prompt for Openclaw Agents?
The Self-Prompt skill addresses a critical limitation in automated agent workflows where agents often ignore messages sent by scripts or cron jobs. In many Openclaw Skills, standard message delivery is treated as a notification, leading agents to categorize them as informational background noise. This skill provides a robust pattern to trigger an actual agent turn, ensuring that every automated alert or scheduled task receives a definitive response.
By transitioning from simple message sending to direct agent invocation, developers can create high-accountability systems. This ensures that monitoring scripts and automated triggers do more than just post text; they drive the agent to analyze data, perform research, and report back findings autonomously. This is a foundational technique for anyone building advanced automation within the ecosystem of Openclaw Skills.
Self-Prompt for Openclaw Agents Use Cases
- Automating accountability checks to ensure agents stay on task without human intervention.
- Forcing agents to investigate and report on system monitoring alerts or performance drops.
- Executing scheduled research tasks and daily summaries via cron jobs.
- Implementing complex multi-step workflows where one script triggers an agent action and captures the result for the next stage.
How Self-Prompt for Openclaw Agents Works
- Replace the standard informational message command with a direct agent execution call.
- The system triggers a mandatory agent turn using the specified session ID and agent identity.
- The agent processes the task and generates a structured response within a defined timeout period.
- The script captures the agent's output and delivers it to the designated communication channel, such as Telegram or Discord.
Self-Prompt for Openclaw Agents Setup
To integrate this into your existing Openclaw Skills, ensure the scripts are accessible in your environment.
# Navigate to your skills directory
cd ~/.openclaw/skills/self-prompt
# Ensure scripts have execution permissions
chmod +x scripts/send_agent_task.sh
You can then call the provided Bash or Python wrappers to initiate forced responses from your agents.
Self-Prompt for Openclaw Agents Data Schema & Taxonomy
The skill utilizes a structured session key format and local logging to manage task execution and history within the broader framework of Openclaw Skills.
| Attribute | Description | Pattern/Path |
|---|---|---|
| Session Key | Unique identifier for the agent context | agent:AGENT_ID:CHANNEL:TYPE:TARGET |
| Task Logs | Local record of triggered agent turns | ~/agent_task.log |
| Timeout | Maximum seconds allowed for agent processing | Integer (default 180s) |
| Channels | Supported communication platforms | Telegram, Discord, etc. |
Self-Prompt for Openclaw Agents Advanced Features
- Data and Task Separation: Send large datasets as informational messages first, followed by a forced agent task to analyze that specific context.
- Python Integration: Importable modules for triggering agent turns directly from complex Python automation scripts.
- Custom Timeouts: Support for long-running research or analysis tasks that exceed standard execution windows.
- Multi-Channel Support: Seamlessly deliver agent responses across different platform types using unified session keys.
SKILL.md
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