A high-throughput skill to process massive Claude API request volumes asynchronously while slashing token costs by 50%.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-claude-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 openclaw-claude-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 Claude Batch API for Anthropic?
The Claude Batch API skill is a powerful tool designed for developers and data scientists who need to process large-scale LLM workloads without the constraints of real-time rate limits or high costs. By leveraging the Openclaw Skills framework, this tool allows for the submission of up to 100,000 requests in a single batch, making it the ideal solution for non-latency-sensitive tasks like document summarization, dataset augmentation, or large-scale content analysis.
This integration streamlines the entire asynchronous lifecycle of Anthropic's Batch API, from request formatting to status polling and final result retrieval. It ensures a 50% discount on input, output, and cached tokens, providing a cost-efficient pathway for high-volume AI operations. When integrated with other Openclaw Skills, it provides a robust foundation for building data-intensive AI pipelines.
Claude Batch API for Anthropic Use Cases
- Bulk content generation for thousands of product descriptions, SEO articles, or summaries
- Large-scale evaluations and automated testing of thousands of model test cases
- Automated content moderation and sentiment analysis for massive user-generated datasets
- Advanced data analysis and insight generation from unstructured enterprise data
- High-volume data transformations such as converting, reformatting, or enhancing legacy datasets
How Claude Batch API for Anthropic Works
- Prepare a list of requests in JSONL format, ensuring each entry has a unique custom_id for easy identification.
- Submit the batch requests to Anthropic's infrastructure via the specialized batch creation endpoint.
- Monitor the processing status through automated polling until the batch transitions to the ended state.
- Retrieve results memory-efficiently by streaming the output from the generated results URL.
- Match the results back to your original dataset using the custom_id and handle any specific request errors or retries.
Claude Batch API for Anthropic Setup
To get started with this skill, ensure you have an active Anthropic API key and a Python 3.8+ environment.
# Export your API key
export ANTHROPIC_API_KEY='sk-ant-...'
# Install the Anthropic Python SDK
pip install anthropic
Configure the Openclaw Skills environment by adding your ANTHROPIC_API_KEY to your local .env file or secrets manager.
Claude Batch API for Anthropic Data Schema & Taxonomy
The skill utilizes the JSONL format for scalability and memory efficiency. The following table describes the primary data structures:
| Component | Format | Description |
|---|---|---|
| Request File | JSONL | Contains the custom_id and params (model, messages, max_tokens) for each task. |
| Response Stream | JSONL | Contains the processing result, including message content or error details linked by custom_id. |
| Metadata | Object | Tracks the batch_id, creation time, processing status, and expiry date (29 days). |
Claude Batch API for Anthropic Advanced Features
- Guaranteed 50% discount on all token usage including input, output, and cache usage
- Support for prompt caching, typically yielding 30-98% additional savings on repeated context
- Full compatibility with Claude vision, tool use, and multi-turn conversations
- Adaptive polling scripts included to minimize API overhead while monitoring batch progress
- Support for large-scale batches up to 256 MB or 100,000 individual requests
SKILL.md
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