使用時期
メール送信をバックグラウンドで実行
affaan-m/ECC
Review django-celery's use cases, installation, workflow, and original source instructions.
npx skills add https://github.com/affaan-m/ECC --skill "docs/ja-JP/skills/django-celery"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Limited to facts supported by the pinned source
Django でのバックグラウンドジョブと非同期処理。
npx skills add https://github.com/affaan-m/ECC --skill "docs/ja-JP/skills/django-celery"The pinned source contains about 55 English words and 9 usable sections. That evidence supports a source profile, not a complete guide.
55 source words · 9 usable sections
What the source actually contains
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
メール送信をバックグラウンドで実行
Review the “セットアップ” section in the pinned source before continuing.
Review the “1. Celery インストール” section in the pinned source before continuing.
Choose a different workflow
Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailMedicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
The source record exposes this install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/ja-JP/skills/django-celery". Inspect the command and pinned source before running it.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Read a plan document, decompose it into steps, design a per-step agent chain from the ECC catalogue, and emit ready-to-paste /orchestrate custom prompts. Generative only — never invokes /orchestrate itself. Use when the user has a multi-step plan and wants to drive it through orchestrate without composing chains by hand.
Django でのバックグラウンドジョブと非同期処理。
pip install celery redis
from celery import shared_task
@shared_task
def send_email(recipient):
# メール送信ロジック
pass
celery -A myapp worker -l info
send_email.delay(recipient) # すぐにキューに追加、非同期実行
from celery.schedules import crontab
app.conf.beat_schedule = {
'send-report-daily': {
'task': 'app.tasks.send_report',
'schedule': crontab(hour=9, minute=0),
},
}
詳細については、ドキュメントを参照してください。