القائمة
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Prism-Shadow/penguin-harness --skill ollamaيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
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.
| 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