django-celery

django-celery is a skill for Claude Code, Codex from Mte90/dotfiles. It costs 44 tokens per session (2,881 once invoked), scanned A, original, no licence file.

A Django integration for Celery, a Python system that runs background tasks outside a web request. It covers distributed work, scheduled tasks with django-celery-beat, monitoring, and recommended practices.

In plain words
What is it for?
Use it for background jobs, periodic tasks, task monitoring, and planning how Celery fits into a Django project.
Why use it?
It helps keep slow or recurring work from delaying responses in a Django application. It also brings task scheduling and monitoring into the Django setup.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it for background jobs, periodic tasks, task monitoring, and planning how Celery fits into a Django project.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mte90/dotfiles/django-celery
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.

Any agent
npx skills add Mte90/dotfiles --skill django-celery
Clone the repo
git clone --depth 1 https://github.com/Mte90/dotfiles

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for django-celery

README.md
[![agentmods](https://agentmods.dev/badge/skills/mte90/dotfiles/django-celery/github.svg)](https://agentmods.dev/skills/mte90/dotfiles/django-celery)
Your own site
<a href="https://agentmods.dev/skills/mte90/dotfiles/django-celery"><img src="https://agentmods.dev/badge/skills/mte90/dotfiles/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.

agentmods 80×15 button for django-celery

Your own site · 80×15
<a href="https://agentmods.dev/skills/mte90/dotfiles/django-celery"><img src="https://agentmods.dev/badge/skills/mte90/dotfiles/django-celery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,881 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found 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 5.1 $0.00044 $0.02881
Opus 5.5 $0.00018 $0.01152
Sonnet 5.5 $0.00009 $0.00576
Haiku 4.5 $0.00004 $0.00288

Measured 28d ago against content hash a4d09a7f073f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

django-celery scanned grade A with 1 finding 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 28d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

with mock.patch('myapp.tasks.requests.get') as mock_get:
.config/opencode/skills/django-celery/SKILL.md · 364 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 28d ago Changed · +149 lines a4d09a7f073f
  2. 1mo ago Changed · -400 lines · +16 tokens per session 3f6e6dda101e
  3. 2mo ago First seen · 615 lines · 28 tokens per session scan A c766d2b715dc

Subscribe to this mod's changes

django-celery is a skill published in the GitHub repository Mte90/dotfiles (51 stars, last pushed 9d ago), with no licence file. It adds 44 tokens to every session and 2,881 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

magic3007/dotfiles · 56 tokens

scaffold-python

Scaffold a complete Python project with CI/CD, release pipeline, justfile, sr.yaml, pyproject.toml, .envrc, and standard files. Uses uv, ruff, and justfile (Python lacks a native task runner like pnpm scripts, so just fills that gap). Loads on top of scaffold-project (run that first for cross-language standard files).…

urmzd/dotfiles · 135 tokens

python-stack

Develop Python projects and PEP 723 scripts: uv, Ruff, ty, Pydantic, pytest tests, async, HTTPX API clients, structlog/OpenTelemetry, LangExtract; fix lint/types.

fmind/dot · 45 tokens

python-web

Build Python web apps and APIs with Django, Litestar, FastAPI, Gradio, or NiceGUI.

fmind/dot · 25 tokens

python-stack

Build typed Python projects with uv, Ruff, ty, pytest, Litestar, and Typer. Use for packages, CLIs, web apps, tests, typing, or API verification.

fmind/dotfiles · 41 tokens

polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

magic3007/dotfiles · 69 tokens