A powerful orchestration skill that enables parallel execution of repetitive batch tasks by dispatching work to multiple subagents simultaneously.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-task
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 multi-task 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 Multi-Task: Parallel Batch Orchestration?
The Multi-Task skill is designed to eliminate the bottlenecks of serial execution in AI agent workflows. Instead of processing items one by one, it identifies independent work units—such as a folder of PDFs or a list of frontend components—and dispatches them as concurrent subagents. This ensures that Openclaw Skills can handle high-volume operations in minutes rather than hours.
By utilizing a sophisticated 6-step workflow, this skill transforms complex, repetitive prompts into organized, parallelized tasks. It handles everything from shared context management to failure isolation, making it an essential tool for developers and data scientists working with large-scale automation.
Multi-Task: Parallel Batch Orchestration Use Cases
- Processing all files in a specific directory (e.g., summarizing PDFs, converting CSVs to JSON).
- Generating multiple frontend pages or components simultaneously from a single design specification.
- Executing repetitive data transformations across dozens of independent inputs or datasets.
- Creating batch reports or documentation from a numbered list of requirements.
How Multi-Task: Parallel Batch Orchestration Works
- Analyze the workload to enumerate all work units, identify the core operation, and detect shared context.
- Plan the decomposition by assigning unique task IDs and creating dedicated output directories using absolute paths.
- Construct self-contained prompts for each subagent so they can operate without access to the main conversation history.
- Dispatch tasks in parallel by including multiple subagent calls in a single message to maximize throughput.
- Monitor progress through waves (grouping tasks into batches of 8-10) and manage automatic retries for failures.
- Merge results and present a comprehensive summary of successful and failed tasks to the user.
Multi-Task: Parallel Batch Orchestration Setup
This skill is an orchestration pattern used within Openclaw Skills. To prepare your environment for high-volume batch operations, it is recommended to create a dedicated output structure:
# Create a parent directory for batch results
mkdir -p ./multi-task-output/
Ensure that the agent has permission to spawn subagents and that absolute paths are used for all file operations to prevent context loss.
Multi-Task: Parallel Batch Orchestration Data Schema & Taxonomy
The skill organizes data using a strict isolation pattern to prevent path conflicts during parallel execution:
| Component | Description |
|---|---|
| task-ID | Sequential identifier (e.g., task-001, task-002) used for tracking. |
| Output Directory | A unique path (e.g., /project/multi-task-output/task-001/) for every unit. |
| Shared Context | Verbatim content (templates/configs) injected into every subagent prompt. |
| Error Logs | Individual error.log files stored within specific task directories if a unit fails. |
All results are sorted by task-ID during the final merge phase to ensure a logical presentation.
Multi-Task: Parallel Batch Orchestration Advanced Features
- Intelligent Skill Matching: Automatically recommends specialized Openclaw Skills (like /pdf, /xlsx, or /frontend-design) to subagents based on the task type.
- Wave-Based Dispatching: Dynamically manages 11-50+ tasks by splitting them into manageable concurrent waves.
- Pilot Execution: Runs 2-3 representative tasks first for large batches to validate prompt quality before full deployment.
- Failure Isolation: Ensures that a failure in one work unit never blocks or affects the execution of other parallel tasks.
- Background Execution: Supports monitoring large-scale operations while allowing the user to perform other actions.
SKILL.md
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