How long do batch jobs typically take to complete?

Anthropic guarantees batch job completion within 24 hours, though processing often finishes much faster depending on current system demand.

Does this skill support vision and tool use within batches?

Yes, the Batch Processing skill supports the full range of Claude's capabilities, including vision, system prompts, and tool use.

How are batch requests formatted?

Requests are structured in JSONL (JSON Lines) format, where each line represents an individual API request with its own unique identifier for tracking results.

What is the primary benefit of the Claude Batch Processing skill?

The main advantage is a 50% reduction in API costs for high-volume, non-time-sensitive workloads like massive document analysis or data enrichment.

What happens if a request within a batch fails?

The skill allows you to retrieve results specifically for 'errored' or 'expired' types, facilitating targeted retries without re-running the entire batch.

Claude Batch Processing

Name: Claude Batch Processing
Author: Lobbi-Docs

byLobbi-Docs

Data Science & ML

Optimizes high-volume workloads by leveraging Anthropic's Message Batches API for 50% cost savings on non-time-sensitive tasks.

About

The Batch Processing skill empowers developers to handle massive workloads—such as processing tens of thousands of documents, generating training data, or performing bulk content analysis—at a fraction of the standard API cost. By utilizing an asynchronous workflow with JSONL files, it enables reliable, high-throughput processing for tasks where immediate responses are not required. It includes comprehensive lifecycle management, from creating batch requests and monitoring status to streaming results and handling retries for errored or expired jobs, making it an essential tool for cost-efficient AI operations at scale.

Key Features

50% cost reduction compared to standard API requests for bulk processing.
Integrated polling and lifecycle tracking for monitoring batch progress.
Automated JSONL request generation and seamless batch submission.
Full feature support including vision, tool use, and custom system prompts.
0 GitHub stars
Asynchronous processing support for workloads exceeding 10,000 documents.

Use Cases

Generating synthetic datasets and training data for machine learning models.
Large-scale document processing, summarization, and metadata extraction.
Running massive model evaluation suites and performance benchmarks.

About

Key Features

50% cost reduction compared to standard API requests for bulk processing.
Integrated polling and lifecycle tracking for monitoring batch progress.
Automated JSONL request generation and seamless batch submission.
Full feature support including vision, tool use, and custom system prompts.
0 GitHub stars
Asynchronous processing support for workloads exceeding 10,000 documents.

Use Cases

Generating synthetic datasets and training data for machine learning models.
Large-scale document processing, summarization, and metadata extraction.
Running massive model evaluation suites and performance benchmarks.