anth-webhooks-events
Implement event-driven patterns with Claude API: streaming SSE events, Message Batches callbacks, and async processing architectures. Use when building real-time Claude integrations or processing batch results. Trigger with phrases like "anthropic events", "claude streaming events", "anthropic async processing", "claude batch callbacks".
Best use case
anth-webhooks-events is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Implement event-driven patterns with Claude API: streaming SSE events, Message Batches callbacks, and async processing architectures. Use when building real-time Claude integrations or processing batch results. Trigger with phrases like "anthropic events", "claude streaming events", "anthropic async processing", "claude batch callbacks".
Teams using anth-webhooks-events should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/anth-webhooks-events/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How anth-webhooks-events Compares
| Feature / Agent | anth-webhooks-events | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implement event-driven patterns with Claude API: streaming SSE events, Message Batches callbacks, and async processing architectures. Use when building real-time Claude integrations or processing batch results. Trigger with phrases like "anthropic events", "claude streaming events", "anthropic async processing", "claude batch callbacks".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Anthropic Events & Async Processing
## Overview
The Claude API does not use traditional webhooks. Instead it provides two event-driven patterns: Server-Sent Events (SSE) for real-time streaming and the Message Batches API for async bulk processing. This skill covers both.
## SSE Streaming Events
```python
import anthropic
client = anthropic.Anthropic()
# Process each SSE event type
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Explain microservices."}]
) as stream:
for event in stream:
match event.type:
case "message_start":
print(f"Started: {event.message.id}")
case "content_block_start":
if event.content_block.type == "tool_use":
print(f"Tool call: {event.content_block.name}")
case "content_block_delta":
if event.delta.type == "text_delta":
print(event.delta.text, end="", flush=True)
elif event.delta.type == "input_json_delta":
print(event.delta.partial_json, end="")
case "message_delta":
print(f"\nStop: {event.delta.stop_reason}")
print(f"Output tokens: {event.usage.output_tokens}")
case "message_stop":
print("[Complete]")
```
## SSE Event Reference
| Event | When | Key Data |
|-------|------|----------|
| `message_start` | Stream begins | `message.id`, `message.model`, `message.usage.input_tokens` |
| `content_block_start` | New block begins | `content_block.type` (text or tool_use), `index` |
| `content_block_delta` | Incremental content | `delta.text` or `delta.partial_json` |
| `content_block_stop` | Block finishes | `index` |
| `message_delta` | Message-level update | `delta.stop_reason`, `usage.output_tokens` |
| `message_stop` | Stream complete | (empty) |
| `ping` | Keepalive | (empty) |
## Async Batch Processing
```python
# Submit batch (up to 100K requests, 50% cheaper)
batch = client.messages.batches.create(
requests=[
{
"custom_id": f"doc-{i}",
"params": {
"model": "claude-sonnet-4-20250514",
"max_tokens": 1024,
"messages": [{"role": "user", "content": f"Summarize: {doc}"}]
}
}
for i, doc in enumerate(documents)
]
)
# Poll for completion
import time
while True:
status = client.messages.batches.retrieve(batch.id)
if status.processing_status == "ended":
break
counts = status.request_counts
print(f"Processing: {counts.processing} | Done: {counts.succeeded} | Errors: {counts.errored}")
time.sleep(30)
# Stream results
for result in client.messages.batches.results(batch.id):
if result.result.type == "succeeded":
print(f"[{result.custom_id}]: {result.result.message.content[0].text[:100]}")
else:
print(f"[{result.custom_id}] ERROR: {result.result.error}")
```
## Event-Driven Architecture Pattern
```python
# Use queues to decouple Claude requests from user-facing endpoints
from redis import Redis
from rq import Queue
redis = Redis()
queue = Queue(connection=redis)
def process_with_claude(prompt: str, callback_url: str):
"""Background job for async Claude processing."""
client = anthropic.Anthropic()
msg = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
# Notify your system via internal callback
import requests
requests.post(callback_url, json={
"text": msg.content[0].text,
"usage": {"input": msg.usage.input_tokens, "output": msg.usage.output_tokens}
})
# Enqueue from your API handler
job = queue.enqueue(process_with_claude, prompt="...", callback_url="https://internal/callback")
```
## Error Handling
| Issue | Cause | Fix |
|-------|-------|-----|
| Stream disconnects | Network timeout | Reconnect and re-request (responses are not resumable) |
| Batch `expired` | Not processed in 24h | Resubmit the batch |
| `errored` results | Individual request was invalid | Check `result.error.message` per request |
## Resources
- [Streaming API](https://docs.anthropic.com/en/api/messages-streaming)
- [Message Batches API](https://docs.anthropic.com/en/api/creating-message-batches)
## Next Steps
For performance optimization, see `anth-performance-tuning`.Related Skills
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