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Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/Prism-Shadow/penguin-harness --skill ollamaコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Use when developing PenguinHarness itself — changing packages/{core,server,web,cli,desktop,landing,docs,skills}, the built-in model catalog, the installers or the release workflow; writing or auditing changelog entries; writing a blog post or capturing release screenshots; deciding what to do about data already on disk; or auditing prose that reads like a leaked authoring session. Covers the two-repo symlink layout, the CI-parity verification chain, the record-and-ship contract, where blog media is hosted, and the seams that are intentional.
Use whenever the user wants to build an agent application — their own program with an embedded agent, such as an AI app, an agentic app or a RAG app. This is writing application code on the Penguin Harness SDK, not configuring an Agent State inside PenguinHarness. Covers self-contained projects, the createSession/run streaming loop with thinking and image messages, wiring the user's existing tools in as CLI commands, and a complete RAG recipe that ingests documents into a knowledge base and answers with citations behind a web UI.
Drive PenguinHarness itself from a shell — list and create agents and sessions, send and steer messages mid-flight, and query costs and scheduled tasks via the penguin CLI over the local server.
SKILL.md を表示中
| name | ollama |
| description | Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents. |
| short_description | Run local models with Ollama. |
| short_description_zh | 用 Ollama 运行本地模型。 |
| version | 2 |
| updated | 2026-08-23T15:42:44.000Z |
Ollama runs open-weight models locally with automatic GPU detection and an OpenAI-compatible API on http://localhost:11434.
If the user's message only invokes this skill (e.g. "use ollama skill") without a concrete request, ask the user what they want. Do not run any command until the goal is clear.
Ask the user which model to run; if they have no preference, recommend the small default Qwen/Qwen3.5-0.8B (ollama pull qwen3.5:0.8b). The model must fit the machine's RAM/VRAM.
Ollama runs everywhere — macOS, Linux and Windows, on CPUs as well as NVIDIA/AMD GPUs — so engine choice follows the user's preference: Ollama is the simple default, while vLLM targets high-throughput GPU serving. Check the current state first:
ollama --version # is Ollama installed?
ollama ps # is the service already serving models?
If port 11434 is already serving, reuse that instance — never kill an existing Ollama process.
qwen3.5:0.8b).ollama pull qwen3.5:0.8b.curl http://localhost:11434/v1/models.penguin config model add ... --client-type openai-chat --base-url http://localhost:11434/v1 — a pulled Ollama model is not visible to Penguin until added.penguin config model list.curl -fsSL https://ollama.com/install.sh | sh # Linux; macOS/Windows use the desktop app
The service then listens on http://localhost:11434.
ollama pull qwen3.5:0.8b # download a model
ollama run qwen3.5:0.8b # interactive chat (pulls first if missing)
ollama list # downloaded models
ollama ps # models loaded in memory
ollama stop qwen3.5:0.8b # unload a model
The endpoint is http://localhost:11434/v1; any non-empty API key is accepted (conventionally ollama):
curl http://localhost:11434/v1/models
The default context window is small, and agent sessions need a large one. Raise it in the server's environment:
OLLAMA_CONTEXT_LENGTH=32768 ollama serve # systemd service: set it via `systemctl edit ollama`
Or bake it into a model variant with a Modelfile:
FROM qwen3.5:0.8b
PARAMETER num_ctx 32768
ollama create qwen3.5-32k -f Modelfile
Model configuration is the penguin CLI's job — penguin config model add registers an endpoint and penguin config model list shows what has been registered. A pulled Ollama model is not visible to Penguin until you add it:
penguin config model add --provider custom --client-type openai-chat \
--base-url http://localhost:11434/v1 --model-id qwen3.5:0.8b --api-key ollama
penguin config model list # the new entry should now be listed