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 MilkyWay008/Hermes-OTG --skill llama-cppgit clone --depth 1 https://github.com/MilkyWay008/Hermes-OTGWrote 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/milkyway008/hermes-otg/llama-cpp)<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/llama-cpp"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/llama-cpp.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00018 | $0.02512 |
| Opus 5 | $0.00009 | $0.01256 |
| Sonnet 5 | $0.00004 | $0.00502 |
| Haiku 4.5 | $0.00002 | $0.00251 |
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 4d 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 \ This is a copy
100% identical to llama-cpp — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
llama.cpp + GGUF
Use this skill for local GGUF inference, quant selection, or Hugging Face repo discovery for llama.cpp.
When to use
- Run local models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs
- Find the right GGUF for a specific Hugging Face repo
- Build a
llama-serverorllama-clicommand from the Hub - Search the Hub for models that already support llama.cpp
- Enumerate available
.gguffiles and sizes for a repo - Decide between Q4/Q5/Q6/IQ variants for the user's RAM or VRAM
Model Discovery workflow
Prefer URL workflows before asking for hf, Python, or custom scripts.
- Search for candidate repos on the Hub:
- Base:
https://huggingface.co/models?apps=llama.cpp&sort=trending - Add
search=<term>for a model family - Add
num_parameters=min:0,max:24Bor similar when the user has size constraints
- Base:
- Open the repo with the llama.cpp local-app view:
https://huggingface.co/<repo>?local-app=llama.cpp
- Treat the local-app snippet as the source of truth when it is visible:
- copy the exact
llama-serverorllama-clicommand - report the recommended quant exactly as HF shows it
- copy the exact
- Read the same
?local-app=llama.cppURL as page text or HTML and extract the section underHardware compatibility:- prefer its exact quant labels and sizes over generic tables
- keep repo-specific labels such as
UD-Q4_K_MorIQ4_NL_XL - if that section is not visible in the fetched page source, say so and fall back to the tree API plus generic quant guidance
- Query the tree API to confirm what actually exists:
https://huggingface.co/api/models/<repo>/tree/main?recursive=true- keep entries where
typeisfileandpathends with.gguf - use
pathandsizeas the source of truth for filenames and byte sizes - separate quantized checkpoints from
mmproj-*.ggufprojector files andBF16/shard files - use
https://huggingface.co/<repo>/tree/mainonly as a human fallback
- If the local-app snippet is not text-visible, reconstruct the command from the repo plus the chosen quant:
- shorthand quant selection:
llama-server -hf <repo>:<QUANT> - exact-file fallback:
llama-server --hf-repo <repo> --hf-file <filename.gguf>
- shorthand quant selection:
- Only suggest conversion from Transformers weights if the repo does not already expose GGUF files.
What ships with it
6 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.
- 4d ago First seen · 250 lines · 18 tokens per session scan A 9c1ef45f6197
llama-cpp is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (14 stars, last pushed 23d ago), licensed MIT. It adds 18 tokens to every session and 2,512 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to llama-cpp, differing in 0 lines, and is treated as a copy.
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