A specialized skill for AI agents to handle high-volume tasks with mandatory progress tracking, checkpointing, and resilient error recovery.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install batch
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 batch 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 Batch Processing?
The Batch skill is a high-reliability protocol designed for AI coding agents to manage and execute tasks involving numerous items, such as bulk file edits, API requests, or data processing. By standardizing the lifecycle of a batch operation—from initial dry runs to final success reporting—this Openclaw Skills entry ensures that long-running tasks are predictable and manageable. It focuses on reducing the risk of silent failures and providing users with clear visibility into the agent's progress.
In addition to execution, the Batch skill emphasizes safety and state management. It enforces confirmation for destructive operations and maintains checkpoints to prevent data loss in the event of an interruption. This makes it an essential tool for developers building complex automation workflows that require more than simple single-turn commands.
Batch Processing Use Cases
- Bulk refactoring of source code across hundreds of individual files
- Processing large datasets via external APIs with built-in rate limit management
- Performing mass file system cleanup or metadata updates in large repositories
- Automated migration of legacy assets where progress monitoring is critical
How Batch Processing Works
- Perform a preliminary dry run with 2-3 items to validate the logic and identify potential issues.
- Calculate the total item count and provide an estimated completion time to the user.
- Request explicit confirmation before proceeding with any destructive operations, such as mass deletions.
- Update the user on progress at regular intervals (every 10 items) to maintain transparency.
- Save the operation state to a checkpoint every 10 to 50 items to enable resumption if interrupted.
- Log failures for specific items while continuing the overall batch to prevent minor errors from stopping the process.
- Generate a final report summarizing successful and failed items, saving errors to a failed.json file for subsequent retries.
Batch Processing Setup
To integrate this batch processing capability into your AI workflow, include the skill definition in your local skill directory. You can set up the required file structure using the following command:
mkdir -p .skills && touch .skills/batch.md
Ensure your agent's system prompt is configured to recognize Openclaw Skills protocols to enable automatic checkpointing and progress reporting during multi-item tasks.
Batch Processing Data Schema & Taxonomy
| Component | Description |
|---|---|
| Progress Interval | User notification triggered every 10 items (e.g., 20/100 complete) |
| Checkpoint Frequency | State serialization every 10-50 items to support recovery |
| failed.json | Structured log of items that failed during processing for retry tasks |
| Retry Logic | Automated 3x backoff (1s, 2s, 4s) for transient network or rate errors |
Batch Processing Advanced Features
- Intelligent failure recovery that distinguishes between skippable data errors and critical system aborts
- Exponential backoff retry logic for handling API rate limits and temporary network timeouts
- Integration with delegation protocols to distribute batch items across multiple sub-agents
- Comprehensive final reporting that outputs retry-ready JSON data for failed items
SKILL.md
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