Swarm Task

What This Skill Does

Swarm Task is a sophisticated orchestration framework designed to supercharge the OpenClaw AI agent by enabling true multi-agent collaboration. Unlike linear task processing, this skill acts as a master controller that evaluates incoming complex requests and decomposes them into logical sub-tasks. It then triggers parallel execution threads, spawning specialized mini-agents to handle specific segments of the work simultaneously. Once individual components are complete, the framework automatically reconciles the data, ensures structural integrity, and aggregates the final output. It also includes an internal logic layer to calculate contribution metrics, providing transparency into how the workload was distributed across the swarm.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/2233admin/swarm-task Ensure your local environment has the necessary permissions to spawn child processes, as this skill requires multi-thread coordination to function effectively.

Use Cases

This skill is ideal for high-complexity, multi-step workflows that exceed the capacity of a single reasoning loop. Typical use cases include:

• Technical Research: Analyzing long-form documentation by having agents concurrently extract different modules of information.
• Content Production: Generating a comprehensive whitepaper by delegating sections like drafting, fact-checking, and formatting to separate sub-agents.
• Data Pipelines: Performing large-scale data cleaning and feature engineering tasks where different subsets of data can be processed in parallel.

Example Prompts

1. "swarm_task: research the history of quantum computing and summarize the key milestones from 1980 to 2024, focusing on hardware and algorithms separately."
2. "complex_task_decompose: create a 5-chapter business plan for a sustainable coffee shop. Use one agent for market research, one for operational strategy, and one for financial projections."
3. "multi_agent_collaboration: analyze the last 100 lines of my project logs for errors and suggest a prioritized fix list using separate agents to categorize bugs by severity."

Tips & Limitations

• Efficiency Gains: Users typically report up to a 300% increase in productivity for complex workflows, but note that the overhead of spawning agents is best suited for tasks lasting longer than 10 seconds.
• Context Windows: While swarm decomposition saves time, ensure your total output requirements don't exceed your current model's output token limits during the aggregation phase.
• System Resources: Since this skill utilizes parallel processing, monitor your local CPU/Memory usage if running very large swarms, as each sub-agent consumes additional system resources.