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llama-cpp

llama-cpp is a skill for Claude Code, Codex from StarryCod/cogitum. It costs 18 tokens per session (2,512 once invoked), scanned A, a copy of llama-cpp, MIT.

A guide for llama.cpp, software that runs compatible language models locally, and GGUF, a model-file format used by it.

In plain words
What is it for?
Use it to discover Hugging Face models, inspect GGUF files, choose a quantization, and build llama-server or llama-cli commands for local inference.
Why use it?
It helps choose a model file and quantization level that fit available CPU, GPU, RAM, or VRAM instead of guessing.

Skill for Claude CodeCodex

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

not rated 12repo 4mo ago A scan 18 tokens copy · 98% MIT

Good fit Use it to discover Hugging Face models, inspect GGUF files, choose a quantization, and build llama-server or llama-cli commands for local inference.

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Install with agentmods
npx agentmods add skills/starrycod/cogitum/llama-cpp
View source ↗ StarryCod/cogitum npmjs.com
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 StarryCod/cogitum --skill llama-cpp
Clone the repo
git clone --depth 1 https://github.com/StarryCod/cogitum

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/starrycod/cogitum/llama-cpp/github.svg)](https://agentmods.dev/skills/starrycod/cogitum/llama-cpp)
Your own site
<a href="https://agentmods.dev/skills/starrycod/cogitum/llama-cpp"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/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/starrycod/cogitum/llama-cpp"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/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 2,512 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 98% 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 $0.02512
Opus 5.5 $0.00007 $0.01005
Sonnet 5.5 $0.00004 $0.00502
Haiku 4.5 $0.00002 $0.00251

Measured 1mo ago against content hash 989057cfcb8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 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 \
Origin

This is a copy

98% identical to llama-cpp — 2 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.

cogitum/data/skills/mlops/inference/llama-cpp/SKILL.md · 250 lines

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-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 · 250 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. 1mo ago First seen · 250 lines · 18 tokens per session scan A 989057cfcb8a

Subscribe to this mod's changes

llama-cpp is a skill published in the GitHub repository StarryCod/cogitum (12 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 2,512 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to llama-cpp, differing in 2 lines, and is treated as a copy.

Data and AILLMagentagenticaianthropiccli
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