AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill django-celerygit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/django-celery)<a href="https://agentmods.dev/skills/ufy2024/auc/django-celery"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/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/ufy2024/auc/django-celery"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/django-celery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 21 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.03198 |
| Opus 5 | $0.00024 | $0.01599 |
| Sonnet 5 | $0.00010 | $0.00640 |
| Haiku 4.5 | $0.00005 | $0.00320 |
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 6d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- django-celery — 89% identical, 28 lines differ
- django-celery — 88% identical, 29 lines differ
- django-celery — 88% identical, 29 lines differ
- django-celery — 88% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 480 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.
- 6d ago First seen · 480 lines · 49 tokens per session scan A a0b0226180df
django-celery is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,198 once invoked, about $0.0002 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 2026-09-03.
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