An intelligent execution layer for AI agents that eliminates waiting times through task classification and parallel sub-agent processing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install parallel-responder
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 parallel-responder 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 Parallel Responder?
Parallel Responder is a high-performance skill designed to transform the AI interaction experience within the Openclaw Skills ecosystem. By adopting a zero-wait philosophy, it ensures that users receive immediate feedback regardless of task complexity. The skill functions as an orchestrator that analyzes incoming requests in real-time to determine the most efficient execution path.
Whether it is a simple status check or a complex multi-step data analysis project, Parallel Responder manages the lifecycle of the task by spawning sub-agents or providing periodic progress reports. This architecture prevents the main interaction thread from hanging, allowing users to continue their dialogue while background processes handle heavy lifting.
Parallel Responder Use Cases
- Managing long-running content creation tasks like writing articles while maintaining an active chat session.
- Performing software installations and environment setups that require background monitoring.
- Organizing large-scale memory or database archives without blocking real-time queries.
- Providing transparent progress tracking for multi-stage technical workflows.
How Parallel Responder Works
- The Task Classifier evaluates the user input using keyword matching and historical data to categorize it as Simple, Medium, or Complex.
- The Time Estimator applies complexity and data volume coefficients to provide a realistic duration window for the task.
- Based on the classification, the system selects an execution strategy: direct reply, execute-and-report, or sub-agent spawning.
- For non-instant tasks, the Progress Reporter initiates a feedback loop, sending Markdown-formatted updates every 10 to 30 seconds.
- Upon completion, the final results are delivered, and the actual execution time is fed back into the adaptive learning module to improve future estimations.
Parallel Responder Setup
To integrate this capability into your Openclaw Skills environment, ensure your gateway supports session spawning. You can configure the classification logic via the following structure:
# Standard installation via the Openclaw CLI
openclaw install parallel-responder
Configure your thresholds in the skill settings:
{
"classification": {
"simple": { "maxTime": 5, "strategy": "direct" },
"medium": { "maxTime": 15, "strategy": "execute-and-report" },
"complex": { "maxTime": 30, "strategy": "sub-agent" }
}
}
Parallel Responder Data Schema & Taxonomy
The skill organizes execution data and metadata as follows:
| Component | Data Type | Description |
|---|---|---|
| Task Category | String | simple, medium, or complex classification |
| Estimation Object | JSON | Contains estimatedTime, confidence score, and complexity factors |
| Progress Report | Markdown | Dynamic template featuring percentage, current step, and remaining time |
| Performance History | Array | Log of historical execution times for adaptive learning adjustment |
Parallel Responder Advanced Features
- Adaptive Learning: Automatically optimizes task classification thresholds based on actual performance history.
- Multi-Agent Spawning: Utilizes independent sub-agents for complex tasks to keep the primary session responsive.
- Multi-dimensional Estimation: Calculates duration using a base time multiplied by complexity and data volume coefficients.
- Dynamic Reporting Intervals: Automatically scales the frequency of progress updates based on total predicted task length.
SKILL.md
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