Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill llama-cppgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/llama-cpp)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/llama-cpp"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/llama-cpp.svg" alt="Measured on agentmods" height="20"></a>- Snyk warn
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 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 \ Copies of this mod
8 near-identical copies found in the catalogue:
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
- llama-cpp — 100% identical, 0 lines differ
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.
- 3d ago First seen · 250 lines · 18 tokens per session scan A 9c1ef45f6197
llama-cpp is a skill published in the GitHub repository NousResearch/hermes-agent (242,680 stars, last pushed today), 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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