llmmanorg

Llmman — AI skill for Claude Code

AI community

Run any agent on any model, models stored as OCI images.

How to install Llmman

This entry records only its repository, not the path inside it, so there is no exact command to give. Open llmmanorg/llmman and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Llmman does

Run any agent on any model, models stored as OCI images.

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README

llmman

Run any agent on any model.

Claude Code, Codex, OpenCode and friends, pointed at a model running on your own machine, or at any hosted provider, in one command.

llmman launch claude --model qwen3.8

That starts a local inference server, downloads a `llama.cpp` build matching your GPU, loads the model, and execs an agent against it.

llmman launch claude --model qwen3.8, answering from a local model

Models are OCI images, so moving one takes no tooling you don't already have:

llmman transfer hf.co/unsloth/Qwen3.5-0.8B-GGUF docker.io/owner/model:latest

That copies from Hugging Face into your own registry directly, without a copy landing in your local store.

Why llmman?

Provider agnostic. The same launch, run and list commands work against a local model or any hosted provider (--provider baseten, --provider groq, ...). One endpoint, one agent config, local or hosted.
Registry agnostic. llmman run qwen3.8 pulls straight from Docker Hub; llmman run hf.co/org/model pulls straight from Hugging Face. Or package a model as a plain OCI artifact and push it to GHCR, quay, Harbor or a self-hosted mirror, then llmman run it from there. No curated library, no account with llmman, no gatekeeper.
Vanilla everything. Upstream llama.cpp releases (or the llama-server already on your PATH), vllm and mlx-lm as-is, serving unmodified GGUF and safetensors files. No fork to wait on, no import step, no private blob format: the store is a standard OCI Image Layout that all can read.
One-step transfer. Any source pull understands paired with any OCI registry