agentmods.dev

llama-cpp

llama-cpp is a skill for Claude Code, Codex from davidtoby/agent-skills. It costs 18 tokens per session (2,501 once invoked), scanned A, a copy of llama-cpp, MIT.

A guide for running local language models with llama.cpp using GGUF model files. GGUF is a model-file format, and llama.cpp runs these models on a computer's CPU or supported graphics hardware.

In plain words
What is it for?
Use it for local inference, model and quantization selection, Hugging Face model discovery, and llama-server or llama-cli commands.
Why use it?
It helps choose a suitable model file and construct the right local command without relying on a hosted model service.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 10repo 1mo ago A scan 18 tokens copy · 97% MIT

Good fit Use it for local inference, model and quantization selection, Hugging Face model discovery, and llama-server or llama-cli commands.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/davidtoby/agent-skills/llama-cpp
View source ↗ davidtoby/agent-skills
Install

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.

Any agent
npx skills add davidtoby/agent-skills --skill llama-cpp
Clone the repo
git clone --depth 1 https://github.com/davidtoby/agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for llama-cpp

README.md
[![agentmods](https://agentmods.dev/badge/skills/davidtoby/agent-skills/llama-cpp/github.svg)](https://agentmods.dev/skills/davidtoby/agent-skills/llama-cpp)
Your own site
<a href="https://agentmods.dev/skills/davidtoby/agent-skills/llama-cpp"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/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.

agentmods 80×15 button for llama-cpp

Your own site · 80×15
<a href="https://agentmods.dev/skills/davidtoby/agent-skills/llama-cpp"><img src="https://agentmods.dev/badge/skills/davidtoby/agent-skills/llama-cpp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,501 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00018 $0.02501
Opus 5 $0.00009 $0.01251
Sonnet 5 $0.00004 $0.00500
Haiku 4.5 $0.00002 $0.00250

Measured 7d ago against content hash 06b21e4aadcf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 7d 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 \
Origin

This is a copy

97% identical to llama-cpp — 1 line 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.

skills/mlops/inference/llama-cpp/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 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-server or llama-cli command from the Hub
  • Search the Hub for models that already support llama.cpp
  • Enumerate available .gguf files 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.

  1. 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:24B or similar when the user has size constraints
  2. Open the repo with the llama.cpp local-app view:
    • https://huggingface.co/<repo>?local-app=llama.cpp
  3. Treat the local-app snippet as the source of truth when it is visible:
    • copy the exact llama-server or llama-cli command
    • report the recommended quant exactly as HF shows it
  4. Read the same ?local-app=llama.cpp URL as page text or HTML and extract the section under Hardware compatibility:
    • prefer its exact quant labels and sizes over generic tables
    • keep repo-specific labels such as UD-Q4_K_M or IQ4_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
  5. Query the tree API to confirm what actually exists:
    • https://huggingface.co/api/models/<repo>/tree/main?recursive=true
    • keep entries where type is file and path ends with .gguf
    • use path and size as the source of truth for filenames and byte sizes
    • separate quantized checkpoints from mmproj-*.gguf projector files and BF16/ shard files
    • use https://huggingface.co/<repo>/tree/main only as a human fallback
  6. 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>
  7. Only suggest conversion from Transformers weights if the repo does not already expose GGUF files.

Read the full file on GitHub · 249 lines

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.

Changes

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.

  1. 7d ago First seen · 249 lines · 18 tokens per session scan A 06b21e4aadcf

Subscribe to this mod's changes

llama-cpp is a skill published in the GitHub repository davidtoby/agent-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 2,501 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 97% identical to llama-cpp, differing in 1 line, and is treated as a copy.

Data and AIagentai-agentsautomationclaudeopenclawskills
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