llama.cpp Benchmark
v1.0.0Run llama.cpp benchmarks on GGUF models to measure prompt processing (pp) and token generation (tg) performance. Use when the user wants to benchmark LLM mod...
⭐ 0· 20·0 current·0 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The skill's scripts and SKILL.md match the stated purpose: finding/building llama.cpp and running llama-bench. One minor inconsistency: the package metadata declares no required binaries, but the build/benchmark scripts assume tools like git, cmake, a C/C++ toolchain, and typical UNIX utilities (find, grep, make). These are expected for building llama.cpp but should be declared.
Instruction Scope
Runtime instructions and scripts are narrowly scoped to cloning/updating the llama.cpp repository, building it, and running llama-bench on local GGUF files. The benchmark script searches the user's home directory and /DATA to locate llama-bench (find ~ /DATA ...) — this is local-only scanning (no remote upload) but may traverse many user files. The build script runs git fetch/pull/clone (network access to GitHub) and compiles code locally; it may prompt interactively and will write under the chosen build directory.
Install Mechanism
No remote arbitrary binary blobs or obscure download hosts are used; the build script clones from github.com/ggerganov/llama.cpp — a known upstream repository — and builds locally via cmake. No extract-from-unknown-URL operations detected.
Credentials
The skill declares no environment variables or credentials. It references an optional LLAMA_BACKEND env var in docs (expected). It does not request or use tokens/secret env vars. Git operations are against a public GitHub repo and should not require credentials.
Persistence & Privilege
The skill is not always-enabled and does not alter other skills or system-wide configuration. It creates/clobbers files under the chosen build directory (default ~/Repo/llama.cpp) and output directory (default ./benchmark_results), which is expected for a build/benchmark tool.
Assessment
This skill appears to do what it says: it will clone/update the llama.cpp GitHub repo and build llama-bench, then run local benchmarks on GGUF files. Before installing: 1) Be prepared to install and run build tools (git, cmake, make/ninja, a C/C++ compiler) — the metadata doesn't list these dependencies. 2) Expect the build to use network access to GitHub and to write files under ~/Repo/llama.cpp and whatever output directory you choose. 3) The benchmark script searches your home directory and /DATA to find llama-bench; this only reads local paths but can traverse many files and may take time. 4) If you need to be extra cautious, review the upstream repository (https://github.com/ggerganov/llama.cpp) and run the build inside a sandbox or VM, and ensure you have sufficient disk space and GPU drivers for the chosen backend.Like a lobster shell, security has layers — review code before you run it.
latestvk9772t3jt2kwek3tjmkbjt0t0s849hr7
License
MIT-0
Free to use, modify, and redistribute. No attribution required.