Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/moltis-org/moltis/llama-cppnpx skills add moltis-org/moltis --skill llama-cppgit clone --depth 1 https://github.com/moltis-org/moltisWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00091 | $0.03991 |
| Opus 5 | $0.00046 | $0.01996 |
| Sonnet 5 | $0.00018 | $0.00798 |
| Haiku 4.5 | $0.00009 | $0.00399 |
Grade A, and why
llama-cpp scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:8080/v1/chat/completions \ How it starts
The opening of the file, as written. The whole thing — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
llama.cpp + GGUF
Pure C/C++ LLM inference with minimal dependencies, plus the GGUF (GPT-Generated Unified Format) standard used for quantized weights. One toolchain covers conversion, quantization, and serving.
When to use
Use llama.cpp + GGUF when:
- Running on CPU-only machines or Apple Silicon (M1/M2/M3/M4) with Metal acceleration
- Using AMD (ROCm) or Intel GPUs where CUDA isn't available
- Edge deployment (Raspberry Pi, embedded systems, consumer laptops)
- Need flexible quantization (2–8 bit with K-quants)
- Want local AI tools (LM Studio, Ollama, text-generation-webui, koboldcpp)
- Want a single binary deploy without Docker/Python
Key advantages:
- Universal hardware: CPU, Apple Silicon, NVIDIA, AMD, Intel
- No Python runtime required (pure C/C++)
- K-quants + imatrix for better low-bit quality
- OpenAI-compatible server built in
- Rich ecosystem (Ollama, LM Studio, llama-cpp-python)
Use alternatives instead:
- vLLM — NVIDIA GPUs, PagedAttention, Python-first, max throughput
- TensorRT-LLM — Production NVIDIA (A100/H100), maximum speed
- AWQ/GPTQ — Calibrated quantization for NVIDIA-only deployments
- bitsandbytes — Simple HuggingFace transformers integration
- HQQ — Fast calibration-free quantization
Quick start
Install
# macOS / Linux (simplest)
brew install llama.cpp
# Or build from source
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make # CPU
make GGML_METAL=1 # Apple Silicon
make GGML_CUDA=1 # NVIDIA CUDA
make LLAMA_HIP=1 # AMD ROCm
# Python bindings (optional)
pip install llama-cpp-python
# With CUDA: CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --force-reinstall --no-cache-dir
# With Metal: CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python --force-reinstall --no-cache-dir
Download a pre-quantized GGUF
# TheBloke hosts most popular models pre-quantized
huggingface-cli download \
TheBloke/Llama-2-7B-Chat-GGUF \
llama-2-7b-chat.Q4_K_M.gguf \
--local-dir models/
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 436 lines · 91 tokens per session scan A f27402ad8529
llama-cpp is a skill published in the GitHub repository moltis-org/moltis (2,841 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 3,991 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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