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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware.
Passed 21 of 23 test cases with the skill, and 15 of 23 without it.
and 3 more in the full report
6 fixed15 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When running `./llama-cli` on an Apple Silicon Mac with Metal support, what layer offload flag value ensures that all model layers are offloaded to the GPU?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies offload flag -ngl 999 | Missed | Passed |
The instructions your model reads when the skill loads.
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal => bashweb_extract => web_fetch, plus web_search when discovery is neededsearch_files => grep_search and glob_searchbrowser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitlyWhen the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.
Use llama.cpp when:
Use TensorRT-LLM instead when:
Use vLLM instead when:
test cases passed, vs 15 / 23 without
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