vintasoftware/django-ai-plugins
2 skills4.5k installs164 starsGitHub
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npx skills add https://github.com/vintasoftware/django-ai-pluginsSkills in this repo
1Django Expertdjango-expert is an agent skill that encodes Django REST Framework best practices for serializers and related API patterns, aimed at solo and indie builders shipping Python backends with Claude Code, Cursor, Codex, or similar agents. It walks through ModelSerializer setup with deliberately listed fields, read-only metadata, and safe exclusions for credentials and internal columns—contrasting those patterns with risky defaults like __all__. Nested serializers are framed as read-only compositions for list and detail payloads, with guidance to split serializers when write paths need different validation or depth. The material is procedural: agents apply it while drafting or refactoring views and serializers in an existing Django repo, not as a one-click generator. Use it during backend implementation whenever you want DRF output that is easier to review, harder to misconfigure for data leaks, and aligned with common production conventions before you move on to tests and deployment.3.9kinstalls2Django Celery ExpertDjango Celery Expert is an agent skill that walks solo and indie builders through integrating Celery into a Django codebase: where celery.py lives, how to load settings with the CELERY_ namespace, and how workers discover tasks across installed apps. It is for anyone shipping a Django API or SaaS who needs emails, reports, or long-running work off the request path without guessing broker URLs or import hooks. Use it while scaffolding or refactoring background jobs when you already have Django settings and want Redis, RabbitMQ, or django-db for results spelled out in copy-pasteable snippets. The guide emphasizes the standard myproject package pattern, explicit serialization and UTC defaults, and a minimal debug task to verify wiring before you add real business tasks—reducing misconfigured brokers and missing celery_app imports that break manage.py and deploy pipelines.656installs