MCPInstall

batch-processing

Message Batches API for Claude with 50% cost savings on bulk processing. Activate for batch jobs, JSONL processing, bulk analysis, and cost optimization.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/thelobbi/claude --skill batch-processing
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads batch-processing automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Batch Processing Skill

Leverage Anthropic's Message Batches API for 50% cost savings on bulk processing workloads.

When to Use This Skill

  • Processing 10K+ documents
  • Model evaluation and benchmarks
  • ETL and data enrichment
  • Training data generation
  • Bulk content analysis
  • Any non-time-sensitive workload

Cost Savings

Processing TypeCost
Standard API100%
Batch API50%

Example: 1M tokens @ $3/1M = $3 (standard) vs $1.50 (batch)

Batch Lifecycle

Created → Processing → Ended (completed/failed/expired/canceled)
  • Processing time: Up to 24 hours
  • Results retention: 29 days
  • Full feature support: Tools, vision, system prompts

Core Implementation

Step 1: Create JSONL File

import json

# Each line is a complete request
requests = [
    {
        "custom_id": "request-1",
        "params": {
            "model": "claude-sonnet-5",
            "max_tokens": 1024,
            "messages": [{"role": "user", "content": "Summarize: Document 1..."}]
        }
    },
    {
        "custom_id": "request-2",
        "params": {
            "model": "claude-sonnet-5",
            "max_tokens": 1024,
            "messages": [{"role": "user", "content": "Summarize: Document 2..."}]
        }
    }
]

# Write JSONL file
with open("batch_requests.jsonl", "w") as f:
    for request in requests:
        f.write(json.dumps(request) + "\n")

Step 2: Submit Batch

import anthropic

client = anthropic.Anthropic()

# Create batch from file
with open("batch_requests.jsonl", "rb") as f:
    batch = client.beta.messages.batches.create(
        requests=f
    )

print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")

Step 3: Poll for Completion

import time

def wait_for_batch(client, batch_id, poll_interval=60):
    """Poll until batch completes"""
    while True:
        batch = client.beta.messages.batches.retrieve(batch_id)

        if batch.processing_status == "ended":
            print(f"Batch completed!")
            print(f"  Succeeded: {batch.request_counts.succeeded}")
            print(f"  Errored: {batch.request_counts.errored}")
            print(f"  Expired: {batch.request_counts.expired}")
            return batch

        print(f"Status: {batch.processing_status} - waiting...")
        time.sleep(poll_interval)

Step 4: Stream Results

def process_results(client, batch_id):
    """Stream and process batch results"""
    results = {}

    for result in client.beta.messages.batches.results(batch_id):
        custom_id = result.custom_id

        if result.result.type == "succeeded":
            message = result.result.message
            results[custom_id] = {
                "status": "success",
                "content": message.content[0].text,
                "usage": message.usage
            }
        elif result.result.type == "errored":
            results[custom_id] = {
                "status": "error",
                "error": result.result.error
            }
        elif result.result.type == "expired":
            results[custom_id] = {
                "status": "expired"
            }

    return results

Complete Workflow

import anthropic
import json
import time

def run_batch_job(documents):
    """Complete batch processing workflow"""
    client = anthropic.Anthropic()

    # 1. Create requests
    requests = []
    for i, doc in enumerate(documents):
        requests.append({
            "custom_id": f"doc-{i}",
            "params": {
                "model": "claude-sonnet-5",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": f"Summarize: {doc}"}]
            }
        })

    # 2. Write JSONL
    with open("batch.jsonl", "w") as f:
        for req in requests:
            f.write(json.dumps(req) + "\n")

    # 3. Submit batch
    with open("batch.jsonl", "rb") as f:
        batch = client.beta.messages.batches.create(requests=f)

    print(f"Batch submitted: {batch.id}")

    # 4. Wait for completion
    while True:
        batch = client.beta.messages.batches.retrieve(batch.id)
        if batch.processing_status == "ended":
            break
        time.sleep(60)

    # 5. Collect results
    results = {}
    for result in client.beta.messages.batches.results(batch.id):
        if result.result.type == "succeeded":
            results[result.custom_id] = result.result.message.content[0].text

    return results

Error Handling

Retry Failed Requests

def retry_failed(client, original_batch_id):
    """Retry failed and expired requests"""
    retry_requests = []

    for result in client.beta.messages.batches.results(original_batch_id):
        if result.result.type in ["errored", "expired"]:
            # Re-create the request (you'll need to store original params)
            retry_requests.append({
                "custom_id": result.custom_id,
                "params": get_original_params(result.custom_id)  # Your implementation
            })

    if retry_requests:
        # Write and submit retry batch
        with open("retry.jsonl", "w") as f:
            for req in retry_requests:
                f.write(json.dumps(req) + "\n")

        with open("retry.jsonl", "rb") as f:
            return client.beta.messages.batches.create(requests=f)

    return None

Error Types

Result TypeAction
succeededProcess normally
erroredCheck error, may retry
expiredRequest took >24h, retry
canceledBatch was canceled

API Reference

Endpoints

MethodEndpointPurpose
POST/v1/messages/batchesCreate batch
GET/v1/messages/batches/{id}Get batch status
GET/v1/messages/batchesList batches
POST/v1/messages/batches/{id}/cancelCancel batch
GET/v1/messages/batches/{id}/resultsStream results

Request Format

{
    "custom_id": "unique-identifier",
    "params": {
        "model": "claude-sonnet-5",
        "max_tokens": 1024,
        "system": "Optional system prompt",
        "messages": [{"role": "user", "content": "..."}],
        "tools": []
    }
}

Best Practices

DO:

  • Use for workloads >100 requests
  • Include unique custom_id for tracking
  • Store original params for retry logic
  • Monitor batch status via polling
  • Process results as streaming JSONL

DON'T:

  • Use for time-sensitive requests
  • Expect immediate results
  • Forget to handle expired requests
  • Ignore error results

Cost Optimization Tips

  1. Batch similar requests - Same model, similar prompts
  2. Combine with caching - Cache static context before batch
  3. Use appropriate model - Haiku for simple tasks, Sonnet for complex
  4. Optimize prompts - Shorter prompts = lower cost

Use Case: Document Processing

# Process 10,000 documents at 50% cost
documents = load_documents("data/*.txt")  # 10K files

# Create batch with consistent format
requests = [{
    "custom_id": doc.id,
    "params": {
        "model": "claude-haiku-4-5",  # Cheapest for bulk
        "max_tokens": 512,
        "system": "Extract key information as JSON.",
        "messages": [{"role": "user", "content": doc.content}]
    }
} for doc in documents]

# Run batch job
results = run_batch_job(requests)
# Cost: ~50% of standard API!

See Also

  • llm-integration - API basics
  • prompt-caching - Cache context
  • streaming - Real-time output

FAQ

What does the batch-processing skill do?

Message Batches API for Claude with 50% cost savings on bulk processing. Activate for batch jobs, JSONL processing, bulk analysis, and cost optimization.

How do I install batch-processing?

Run npx skills add https://github.com/thelobbi/claude --skill batch-processing in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/batch-processing/ (personal) or .claude/skills/batch-processing/ (project).

Which AI agents can use batch-processing?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is batch-processing safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains batch-processing?

It is published by thelobbi in thelobbi/claude. It has 64 recorded installs.

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