Agent skill · magnus919
llama-cpp
Operate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. Use when installing or building llama.cpp, selecting or inspecting GGUF models, running llama-cli, serving an OpenAI-compatible API with llama-server, tuning memory and performance, or diagnosing backend, context, template, and API failures. Do not use for model training or fine-tuning, general inference-framework selection, llama-cpp-python or other bindings, LlamaIndex, Ollama, or LM Studio operation.
What it needs
About 5k tokens when loaded.
What this skill does
llama.cpp Operations Treat every launch recipe as a hypothesis about a specific build, model, host, and workload. Discover capabilities from the installed binary, inspect the model and startup logs, then measure the requested boundary. Operating contract 1. Record the exact llama.cpp version or commit, installation method, OS and architecture, CPU and RAM, accelerator and memory, driver/toolkit, available devices, model provenance and quantization, intended context, concurrency, and workload. 2. Read the installed command's --help before using a flag from documentation. llama.cpp flags, defaults, binary names, and REST behavior change frequently. 3. Confirm the target, scope, and rollback path before acting. Read-only discovery may proceed without confirmation. 4. Verify the backend from --list-devices and model-load logs. A successful build or an accepted GPU flag does not prove acceleration is active. 5. Start with a bounded CLI smoke test on loopback or local input. Establish a measured baseline before changing threads, batches, context, cache types, offload, or split mode. 6. Call work complete only at the requested boundary: binary, model load, generated output, API response, benchmark comparison, or diagnosed failure with evidence. When not to use Use ml-engineering for model training, fine-tuning, broad quantization methodology, evaluation design, or choosing among llama.cpp, vLLM, TGI, and other engines. Use the relevant product skill for Ollama, LM Studio, or LlamaIndex. Use binding-specific documentation for llama-cpp-python, node-llama-cpp, or other language wrappers. Read-only preflight Run only commands that exist in the installed build: Also inspect host memory and accelerator state with native OS/vendor tools. Record results in the operation record. If no binary exists, choose an installation path only after reading installation and backends. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/llama-cpp