Moltis is a persistent personal agent server written in Rust that runs on hardware controlled by its user. It provides an AI agent with sandboxed command execution, model-provider connections, memory, voice, scheduling, messaging integrations, browser automation, and MCP tools. Its catalogue add-ons extend the agent’s workflows and available tools.
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 skills add moltis-org/moltis --skill llama-cppgit clone --depth 1 https://github.com/moltis-org/moltisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/moltis-org/moltis/llama-cpp)<a href="https://agentmods.dev/skills/moltis-org/moltis/llama-cpp"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/llama-cpp/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/moltis-org/moltis/llama-cpp"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/llama-cpp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Excessive Agency · line 294 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
- medium Data Exfiltration · line 112 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Rogue Agent · line 365 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
What 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 5.1 | $0.00091 | $0.03991 |
| Opus 5.5 | $0.00036 | $0.01596 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (2,906 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 3,991 once invoked, about $0.0004 per session on Opus 5.5. 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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