llm-inference-scaling

The skill target is coherent: it describes a legitimate DevOps pattern for autoscaling GPU-backed LLM inference on Kubernetes using standardized tooling (KEDA, Prometheus, Redis, NVIDIA GPU Operator). The footprint is proportionate to the stated purpose, with normal credential handling (Kubernetes secrets) and no evident credential leakage, free-hosted executables, or malicious data flows. While the pattern has operational risks inherent to cluster autoscaling (e.g., spot-driven evictions), these are addressed by practices in the guidance (PDB, pre-warming, monitoring). Overall verdict: BENIGN with MEDIUM risk considerations related to secret management and autoscaler behavior that should be tested in a controlled environment.