model-deploy
Deploy AI/ML models on OpenShift AI using KServe with vLLM, NVIDIA NIM, or Caikit+TGIS runtimes. Use when: - "Deploy Llama 3 on my cluster" - "Set up a vLLM inference endpoint" - "Deploy a model with NIM" - "Create an InferenceService for Granite" - "I need to serve a model on OpenShift AI" Handles runtime selection, GPU validation, InferenceService CR creation, and rollout monitoring. NOT for NIM platform setup (use /nim-setup first). NOT for custom runtime creation (use /serving-runtime-config).
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Low-risk findings worth noting
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The required workflow uses the `WebFetch` tool in Step 4 to retrieve hardware specs from arbitrary web URLs (`https://build.nvidia.com/models` and documentation pages) when a model is not found in the local cache.
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- RHEcosystemAppEng/agentic-plugins
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- Security analysis
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