openclaw-claude-batch

What This Skill Does

The openclaw-claude-batch skill provides a powerful interface for interacting with the Claude Batch API. This skill is specifically designed to handle large-scale LLM workloads asynchronously, allowing you to process thousands of requests without hitting standard rate limits or incurring the full cost of real-time API requests. By offloading heavy processing tasks, you can achieve significant infrastructure savings and operational efficiency. The skill handles the complexities of batch creation, polling for status updates, and parsing retrieved results, ensuring your workflow remains reliable and memory-efficient.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal: clawhub install openclaw/skills/skills/kai-tw/openclaw-claude-batch Ensure you have the required anthropic SDK installed and your API credentials properly configured in your local environment variables before executing the install command.

Use Cases

• Bulk content generation: Efficiently generate thousands of product descriptions, blog posts, or marketing variations at 50% cost.
• Large-scale evaluations: Run complex test suites or model comparisons against thousands of inputs to generate robust performance metrics.
• Content moderation: Filter, classify, or flag vast datasets of user-generated content for safety and policy compliance.
• Data analysis: Execute complex transformations, summarizations, or entity extraction tasks across massive datasets.

Example Prompts

1. "Run a batch process on feedback_data.jsonl using Claude-3.5-Sonnet and store the results in analyzed_feedback.jsonl."
2. "Create a new Claude batch request for the 500 items in my queue and monitor its status until completion."
3. "Summarize the batch processing results stored in batch_id_123 and save the errors to a separate report."

Tips & Limitations

• Cost Efficiency: Always utilize batching for non-time-sensitive tasks to leverage the 50% discount on token usage.
• Data Handling: Use the custom_id field in your JSONL file to map request inputs to specific outputs, as return order is not guaranteed.
• Persistence: Remember that results are only available for 29 days after creation, so ensure you download and store your outputs before they expire.
• Limits: Batches are capped at 100,000 requests or 256MB per file. Plan your data segmentation accordingly if exceeding these limits.