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 skills add gongyijie85/dsh-ecc --skill django-celerygit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gongyijie85/dsh-ecc/django-celery)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/django-celery"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/django-celery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/django-celery"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/django-celery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.03099 |
| Opus 5.5 | $0.00020 | $0.01240 |
| Sonnet 5.5 | $0.00010 | $0.00620 |
| Haiku 4.5 | $0.00005 | $0.00310 |
Grade A, and why
django-celery 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 1mo 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.
This is a copy
88% identical to django-celery — 29 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.
How it starts
The opening of the file, as written. The whole thing — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Django + Celery Async Task Patterns
Production-grade patterns for background task processing in Django using Celery with Redis or RabbitMQ.
When to Activate
- Adding background jobs or async processing to a Django app
- Implementing periodic/scheduled tasks
- Offloading slow operations (email, PDF generation, API calls) from request cycle
- Setting up Celery Beat for cron-like scheduling
- Debugging task failures, retries, or queue backlogs
- Writing tests for Celery tasks
Project Setup
Installation
pip install 'celery[redis]' django-celery-results django-celery-beat
celery.py — App Entrypoint
# config/celery.py
import os
from celery import Celery
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings.development')
app = Celery('myproject')
app.config_from_object('django.conf:settings', namespace='CELERY')
app.autodiscover_tasks() # Discovers tasks.py in each INSTALLED_APP
@app.task(bind=True, ignore_result=True)
def debug_task(self):
print(f'Request: {self.request!r}')
# config/__init__.py
from .celery import app as celery_app
__all__ = ('celery_app',)
Django Settings
# config/settings/base.py
# Broker (Redis recommended for production)
CELERY_BROKER_URL = env('CELERY_BROKER_URL', default='redis://localhost:6379/0')
CELERY_RESULT_BACKEND = env('CELERY_RESULT_BACKEND', default='django-db')
# Serialization
CELERY_ACCEPT_CONTENT = ['json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
# Task behavior
CELERY_TASK_TRACK_STARTED = True
CELERY_TASK_TIME_LIMIT = 30 * 60 # Hard limit: 30 min
CELERY_TASK_SOFT_TIME_LIMIT = 25 * 60 # Soft limit: sends SoftTimeLimitExceeded
CELERY_WORKER_PREFETCH_MULTIPLIER = 1 # Prevent worker hoarding long tasks
CELERY_TASK_ACKS_LATE = True # Re-queue on worker crash
# Result persistence
CELERY_RESULT_EXPIRES = 60 * 60 * 24 # Keep results 24 hours
# Beat scheduler (for periodic tasks)
CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'
# Installed apps
INSTALLED_APPS += [
'django_celery_results',
'django_celery_beat',
]
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.
- 1mo ago First seen · 459 lines · 49 tokens per session scan A 1f9189ece910
django-celery is a skill published in the GitHub repository gongyijie85/dsh-ecc (9 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,099 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to django-celery, differing in 29 lines, and is treated as a copy.
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