Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sawrus/agent-guides/async-processingnpx skills add sawrus/agent-guides --skill async-processinggit clone --depth 1 https://github.com/sawrus/agent-guidesWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.01194 |
| Opus 5 | $0.00009 | $0.00597 |
| Sonnet 5 | $0.00004 | $0.00239 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade A, and why
async-processing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Async Processing Skill
Expertise: Task queues (Celery, ARQ, Dramatiq), Kafka/NATS consumers, background jobs, retry strategies, idempotency, dead-letter queues.
When to Use Async Processing
Use async when:
✅ Operation takes > 200ms (send email, resize image, call slow 3rd party)
✅ Work can be retried independently (payment webhook, notification)
✅ Decoupling producers from consumers is required
✅ Fan-out to multiple consumers needed
Keep synchronous:
❌ Response depends on the result (user sees outcome immediately)
❌ Must be transactional with the triggering DB write
Task Queue (Celery + Redis)
# tasks.py
from celery import Celery
from celery.utils.log import get_task_logger
app = Celery("myapp", broker="redis://localhost:6379/1", backend="redis://localhost:6379/2")
app.conf.update(
task_serializer="json",
result_expires=3600,
task_acks_late=True, # Ack after completion, not on receive
task_reject_on_worker_lost=True,
task_default_retry_delay=60, # 1 min base delay
task_max_retries=5,
)
logger = get_task_logger(__name__)
@app.task(bind=True, max_retries=5, default_retry_delay=30)
def send_order_confirmation(self, order_id: int) -> None:
try:
order = Order.objects.get(id=order_id)
email_service.send_confirmation(order)
logger.info("email.sent", extra={"order_id": order_id})
except EmailServiceError as exc:
# Exponential backoff: 30s, 60s, 120s, 240s, 480s
delay = 30 * (2 ** self.request.retries)
raise self.retry(exc=exc, countdown=delay)
except Order.DoesNotExist:
logger.error("order.not_found", extra={"order_id": order_id})
# Don't retry — data issue, not transient
Message Consumer (Kafka / aiokafka)
from aiokafka import AIOKafkaConsumer
import asyncio, json
async def consume_order_events():
consumer = AIOKafkaConsumer(
"orders.events",
bootstrap_servers="kafka:9092",
group_id="notification-service",
auto_offset_reset="earliest",
enable_auto_commit=False, # Manual commit — control exactly-once
value_deserializer=lambda v: json.loads(v.decode()),
)
await consumer.start()
try:
async for msg in consumer:
event = msg.value
try:
await handle_event(event)
await consumer.commit() # Only commit on success
except TransientError as e:
logger.warning("event.retry", event_type=event["type"], error=str(e))
await asyncio.sleep(5) # Back off, do NOT commit
except PermanentError as e:
logger.error("event.dead_letter", event=event, error=str(e))
await dead_letter_queue.publish(event)
await consumer.commit() # Commit to move past poison message
finally:
await consumer.stop()
# Idempotency — always check before processing
async def handle_event(event: dict) -> None:
event_id = event["event_id"]
if await redis.exists(f"processed:{event_id}"):
return # Already handled — skip
await process(event)
await redis.setex(f"processed:{event_id}", 86400, "1")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 153 lines · 18 tokens per session scan A 15934afbaa2b
async-processing is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 11d ago), licensed MIT. It adds 18 tokens to every session and 1,194 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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