A powerful task orchestration skill designed to distribute independent workloads across multiple AI agents simultaneously and aggregate their results.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pilot-task-parallel
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 pilot-task-parallel 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 pilot-task-parallel?
The pilot-task-parallel skill is a robust orchestration layer for developers using the pilot-protocol. It enables a fan-out architecture where complex or high-volume workloads are split into independent chunks and dispatched to a swarm of agents. This approach significantly reduces total execution time by leveraging the concurrent processing power of multiple peer nodes.
By utilizing Openclaw Skills like this one, engineers can transform sequential bottlenecks into efficient parallel streams. The skill manages the lifecycle of distributed tasks, from initial submission and status polling to the final data aggregation phase, ensuring a cohesive output from a fragmented execution environment.
pilot-task-parallel Use Cases
- Distributing compute-heavy image or video processing across multiple GPU-enabled agents.
- Running massive data scraping or research operations by querying multiple agents in parallel.
- Performing large-scale code analysis or testing suites where independent modules can be verified concurrently.
- Reducing latency in complex multi-agent workflows that do not have strict sequential dependencies.
How pilot-task-parallel Works
- Discovery: Identify available peer agents within the network using pilotctl peer search queries.
- Distribution: Submit unique task payloads to each selected agent using a loop-based fan-out mechanism.
- Tracking: Monitor the status of each Task ID individually by polling the task list for completion or failure states.
- Synchronization: Implement a wait-state logic to ensure all parallel threads have finished their execution cycle.
- Aggregation: Query the final results from all successful tasks and use jq to merge the JSON outputs into a single consolidated report.
pilot-task-parallel Setup
To utilize this skill, ensure you have the pilotctl binary installed and the daemon running. This skill relies on Bash 4.0+ and jq for JSON processing.
# Start the pilot-protocol daemon
pilotctl daemon start
# Ensure you have the required dependencies
sudo apt-get install jq bash
# Verify peer connectivity before running parallel tasks
pilotctl peers list
pilot-task-parallel Data Schema & Taxonomy
The skill interacts with the pilot-protocol task management system using the following data structures:
| Property | Type | Description |
|---|---|---|
task_id |
UUID | Unique identifier for the individual parallel task segment. |
status |
String | Current state: submitted, completed, or failed. |
result |
JSON Object | The specific output data returned by the executing agent. |
address |
String | The network address of the peer agent handling the task. |
pilot-task-parallel Advanced Features
- Load Balancing: Distribute tasks using modulo arithmetic to ensure even workload distribution across available agents.
- Status Polling Logic: Built-in shell logic to wait for multi-agent completion before proceeding to downstream logic.
- Error Handling: Ability to identify failed task IDs within a parallel batch for targeted retries.
- Selective Merging: Advanced jq filtering to aggregate only successful results while discarding or logging failures from specific Openclaw Skills nodes.
SKILL.md
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