What problem does it solve? Turning a plain Python function into a distributed, queue-backed task normally requires learning complex worker frameworks. This Skill guides the AI to write correct funboost code using the @boost decorator and BoosterParams, avoiding common mistakes like passing raw arguments to @boost or misusing push/publish. ## Core Features & Use Cases - Task Definition: Create distributed task functions with @boost(BoosterParams(...)) supporting 40+ message queue brokers, concurrency modes, QPS limits, and retries. - Message Publishing: Publish tasks with push, publish with TaskOptions (countdown, custom task_id), or async aio_push/aio_publish. - Consumer Management: Start single or multiple consumers, use multi-process consumption, and access task context via fct. - Use Case: You need to crawl 100 URLs concurrently with rate limiting and automatic retries. The Skill produces a @boost-decorated function with concurrent_num, qps, and max_retry_times configured, plus push and consume calls. ## Quick Start Write a funboost distributed task using @boost with BoosterParams that consumes URLs from a Redis queue with 30 concurrent workers and a QPS limit of 10.
