Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
90
93%
Does it follow best practices?
Impact
73%
1.25xAverage score across 3 eval scenarios
Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
The required runtime workflow fetches Hugging Face Hub web pages and API JSON (e.g., `https://huggingface.co/<repo>?local-app=llama.cpp` and `https://huggingface.co/api/models/<repo>/tree/main?...`), which are outsider-authored content from third-party model repos and can include arbitrary README/text/HTML that may be ingested into the agent’s LLM context.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
The skill explicitly runs git clone/make on https://github.com/ggml-org/llama.cpp (fetching and building remote code) and instructs runtime use of the Hugging Face tree API (https://huggingface.co/api/models/<repo>/tree/main?recursive=true) to drive model/file selection, so these URLs are runtime dependencies that fetch code or data which directly determine execution or agent behavior.
0448a7c
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.