celery-task-structure

A set of coding rules for writing Celery tasks, which are background jobs that run outside a web request.

In plain words
What is it for?
It guides tasks such as processing orders, synchronizing inventory from an external service, and generating scheduled reports.
Why use it?
It encourages consistent task definitions with type hints, documentation, logging, retries, rate limits, and clear return values. This makes background work easier to operate and maintain.

Cursor rule for Cursor

Install

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.

agentmods
npx agentmods add rules/technickai/ai-coding-config/celery-task-structure
Clone the repo
git clone --depth 1 https://github.com/TechNickAI/ai-coding-config

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 572 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00572
Opus 5 $0.00000 $0.00286
Sonnet 5 $0.00000 $0.00114
Haiku 4.5 $0.00000 $0.00057

Measured 2d ago against content hash 338e9f1bbf67, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

celery-task-structure 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.

Origin

This is a copy

100% identical to celery-task-structure — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.cursor/rules/python/celery-task-structure.mdc · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Celery Task Structure

Task Definition

@shared_task(max_retries=3, default_retry_delay=60)
def process_order(order_id: int):
    """Process order and send confirmation email."""
    logger.info(f"Processing order {order_id}")
    order = Order.objects.get(id=order_id)
    order.process_payment()
    send_confirmation_email(order)
    logger.info(f"Order {order_id} complete")
    return {"status": "success", "order_id": order_id}

@shared_task(rate_limit="10/m")
def sync_inventory(product_id: int):
    """Sync product inventory from external API."""
    product = Product.objects.get(id=product_id)
    inventory_data = fetch_inventory_from_api(product.sku)
    product.stock_count = inventory_data["quantity"]
    product.save()
    return {"product_id": product_id, "stock": product.stock_count}

@shared_task
def generate_daily_report(date: str):
    """Generate and email daily sales report."""
    report_date = datetime.fromisoformat(date)
    report = compile_sales_report(report_date)
    send_report_email(report)
    return {"date": date, "orders": report.order_count}

Best Practices

We:

  • Use type hints for parameters
  • Include helpful docstrings
  • Log start and completion
  • Set appropriate retry settings
  • Consider rate limits for external APIs
  • Pass IDs, not objects (objects can't be serialized)
  • Return serializable data (dicts, not model instances)

Error Handling

Default: let exceptions bubble up. Celery will retry based on max_retries setting.

For specific retry behavior:

@shared_task(max_retries=3, default_retry_delay=60)
def sync_external_data(data_id: int):
    """Sync data from external API with retry on transient failures."""
    data = Data.objects.get(id=data_id)  # Let it crash if data doesn't exist
    try:
        result = external_api_call(data)
        return result
    except ExternalAPIError as exc:
        # Retry on transient API errors
        raise self.retry(exc=exc)

Honeybadger/Sentry will alert us on failures. Let tasks crash on real problems.

Read the full file on GitHub · 88 lines

Changes

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.

  1. 2d ago First seen · 88 lines · 0 tokens per session scan A 338e9f1bbf67

Subscribe to this mod's changes

celery-task-structure is a cursor rule published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 572 tokens. A static security scan graded it A with 0 findings. It is 100% identical to celery-task-structure, differing in 2 lines, and is treated as a copy.