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Found 20 Skills
Expert Django and Celery guidance for asynchronous task processing. Use when designing background tasks, configuring workers, handling retries and errors, optimizing task performance, implementing periodic tasks, or setting up production monitoring. Follows Celery best practices with Django integration patterns.
Expert Celery distributed task queue engineer specializing in async task processing, workflow orchestration, broker configuration (Redis/RabbitMQ), Celery Beat scheduling, and production monitoring. Deep expertise in task patterns (chains, groups, chords), retries, rate limiting, Flower monitoring, and security best practices. Use when designing distributed task systems, implementing background job processing, building workflow orchestration, or optimizing task queue performance.
Distributed task queue system for Python enabling asynchronous execution of background jobs, scheduled tasks, and workflows across multiple workers with Django, Flask, and FastAPI integration.
Advanced Celery patterns including canvas workflows, priority queues, rate limiting, multi-queue routing, and production monitoring. Use when implementing complex task orchestration, task prioritization, or enterprise-grade background processing.
Celery task patterns including task definition, retry strategies, periodic tasks, and best practices. Use when implementing background tasks, scheduled jobs, or async processing.
Master Django 5.x with async views, DRF, Celery, and Django Channels. Build scalable web applications with proper architecture, testing, and deployment. Use PROACTIVELY for Django development, ORM optimization, or complex Django patterns.
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
Prowler API patterns: RLS, RBAC, providers, Celery tasks. Trigger: When working in api/ on models/serializers/viewsets/filters/tasks involving tenant isolation (RLS), RBAC, or provider lifecycle.
Backend de mensajería para Celery con baja latencia
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
Expert in background job processing with Bull/BullMQ (Redis), Celery, and cloud queues. Implements retries, scheduling, priority queues, and worker management. Use for async task processing, email campaigns, report generation, batch operations. Activate on "background job", "async task", "queue", "worker", "BullMQ", "Celery". NOT for real-time WebSocket communication, synchronous API calls, or simple setTimeout operations.
Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.