Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
8.7
Rating
0
Installs
DevOps & Infrastructure
Category
Excellent skill for Modal serverless platform. The description is comprehensive and actionable, enabling a CLI agent to understand when and how to invoke Modal capabilities. Task knowledge is outstanding with detailed code examples, GPU configurations, scaling patterns, and complete workflows for ML deployment, batch processing, and scheduled jobs. Structure is very clear with a well-organized overview, core capabilities, and properly externalized reference documentation. Novelty is high - Modal's GPU-accelerated serverless containers, autoscaling, and ML-specific features would require significant tokens and expertise for a CLI agent to implement from scratch. The skill effectively encapsulates complex infrastructure patterns into simple decorators. Minor improvement possible in explicitly cross-referencing the 11 reference files within relevant sections, though the current reference section at the end is adequate. Overall, this is a high-quality skill that meaningfully reduces complexity for cloud ML/compute workloads.
davila7
Skill Author
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