Anth core workflow b
Skill ComeOnOliver/skillshub/skills/jeremylongshore/claude-code-plugins-plus-skills/anth-core-workflow-b
Build Claude streaming and Message Batches API workflows. Use when implementing real-time streaming responses, SSE event handling, or processing bulk requests with the 50% cheaper Batches API. Trigger with phrases like "claude streaming", "anthropic batch", "message batches api", "SSE events anthropic", "stream claude response".From its SKILL.md
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SKILL.md
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Anthropic Core Workflow B — Streaming & Batches
Overview
Two complementary patterns: real-time streaming for interactive UIs (SSE events via POST /v1/messages with stream: true) and the Message Batches API (POST /v1/messages/batches) for processing up to 100,000 requests asynchronously at 50% cost reduction.
Prerequisites
- Completed
anth-install-authsetup - Familiarity with
anth-core-workflow-a(Messages API basics) - For batches: understanding of async/polling patterns
Instructions
Streaming — Python SDK
import anthropic
client = anthropic.Anthropic()
# Method 1: High-level streaming (recommended)
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=2048,
messages=[{"role": "user", "content": "Write a short story about a robot."}]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
# After stream completes, access full message
final_message = stream.get_final_message()
print(f"\nUsage: {final_message.usage.input_tokens}+{final_message.usage.output_tokens}")
# Method 2: Event-level streaming (for custom event handling)
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=2048,
messages=[{"role": "user", "content": "Explain REST APIs."}]
) as stream:
for event in stream:
if event.type == "content_block_delta":
if event.delta.type == "text_delta":
print(event.delta.text, end="")
elif event.type == "message_stop":
print("\n[Stream complete]")
Streaming — TypeScript SDK
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
// High-level streaming
const stream = client.messages.stream({
model: 'claude-sonnet-4-20250514',
max_tokens: 2048,
messages: [{ role: 'user', content: 'Write a haiku about code.' }],
});
stream.on('text', (text) => process.stdout.write(text));
stream.on('finalMessage', (msg) => {
console.log(`\nTokens: ${msg.usage.input_tokens}+${msg.usage.output_tokens}`);
});
await stream.finalMessage();
Streaming with Tool Use
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
tools=tools, # Same tools array from core-workflow-a
messages=[{"role": "user", "content": "What's the weather?"}]
) as stream:
for event in stream:
if event.type == "content_block_start":
if event.content_block.type == "tool_use":
print(f"Tool call: {event.content_block.name}")
elif event.type == "content_block_delta":
if event.delta.type == "input_json_delta":
print(event.delta.partial_json, end="") # Tool input arrives incrementally
Message Batches API — Bulk Processing
# Create a batch of up to 100,000 requests (50% cost savings)
batch = client.messages.batches.create(
requests=[
{
"custom_id": "req-001",
"params": {
"model": "claude-sonnet-4-20250514",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Summarize: ...article1..."}]
}
},
{
"custom_id": "req-002",
"params": {
"model": "claude-sonnet-4-20250514",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Summarize: ...article2..."}]
}
},
# ... up to 100,000 requests
]
)
print(f"Batch ID: {batch.id}") # msgbatch_01HBMt...
print(f"Status: {batch.processing_status}") # in_progress
print(f"Counts: {batch.request_counts}") # {processing: 2, succeeded: 0, ...}
Poll for Batch Completion
import time
while True:
batch_status = client.messages.batches.retrieve(batch.id)
if batch_status.processing_status == "ended":
break
print(f"Processing... {batch_status.request_counts}")
time.sleep(30)
# Stream results (returns JSONL)
for result in client.messages.batches.results(batch.id):
if result.result.type == "succeeded":
text = result.result.message.content[0].text
print(f"[{result.custom_id}]: {text[:100]}...")
elif result.result.type == "errored":
print(f"[{result.custom_id}] ERROR: {result.result.error}")
SSE Event Types Reference
| Event | Description | Key Fields |
|---|---|---|
message_start | Stream begins | message.id, message.model, message.usage |
content_block_start | New content block | content_block.type (text/tool_use) |
content_block_delta | Incremental content | delta.text or delta.partial_json |
content_block_stop | Block complete | index |
message_delta | Message-level update | delta.stop_reason, usage.output_tokens |
message_stop | Stream complete | (empty) |
ping | Keepalive | (empty) |
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Stream disconnects mid-response | Network timeout | Implement reconnection with partial content |
Batch expired status | Not processed within 24h | Resubmit batch |
errored results in batch | Individual request invalid | Check result.error for each failed request |
| 429 on batch creation | Too many concurrent batches | Wait; limit is ~100 concurrent batches |
Resources
Next Steps
For common errors, see anth-common-errors.
What ships with it
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Just SKILL.md. No reference files, no scripts.