Page Agent
Tommy-yw/RunbookHermes
Embed alibaba/page-agent into your own web application — a pure-JavaScript in-page GUI agent that ships as a single <script tag or npm package and lets end-users of your site drive the UI with…
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
Install Ollama
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Prism-Shadow/penguin-harness ollama --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/model-development/skills/ollama .claude/skills/ollama && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .claude/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollamaType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Prism-Shadow/penguin-harness ollama --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/model-development/skills/ollama .agents/skills/ollama && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .agents/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Prism-Shadow/penguin-harness ollama --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/model-development/skills/ollama .cursor/skills/ollama && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .cursor/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Prism-Shadow/penguin-harness.git --path plugins/model-development/skills/ollama--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Prism-Shadow/penguin-harness ollama --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/model-development/skills/ollama .gemini/skills/ollama && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .gemini/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Prism-Shadow/penguin-harness ollamaInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/model-development/skills/ollama .github/skills/ollama && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .github/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Prism-Shadow/penguin-harness --skill ollama -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Prism-Shadow/penguin-harness ollama --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/model-development/skills/ollama .opencode/skills/ollama && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ollama" agent skill from https://github.com/Prism-Shadow/penguin-harness/tree/main/plugins/model-development/skills/ollama into .opencode/skills/ollama/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ollama", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ollama- GitHub stars
- 2.5k
- Token cost
- ~839 tokens
- SKILL.md length
- 305 words
- Files
- 1
- Skills in repo
- 31
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
- Works in 6 steps: Ask the user which model to run; with no… → Pick the engine the user prefers: Ollama… → Install Ollama if missing, then ollama… → …
- Tasks that involve LLM inference and serving
- SKILL.md covers Before you start, Suggested workflow, Install and Pull and run, plus 3 more sections
- Calls ollama, curl and sh; reaches ollama.com
What it does
Ollama is an agent skill from Prism-Shadow/penguin-harness. Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
Its SKILL.md is about 840 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering LLM inference and serving. It works with Ollama, OpenAI and Qwen. The repository describes itself as: 🐧 Unified and Stable RSI Platform. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve LLM inference and serving
Example prompts
- “/ollama”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Ask the user which model to run; with no preference, recommend Qwen/Qwen3.5-0.8B (qwen3.5:0.8b).
- Pick the engine the user prefers: Ollama is the default; vLLM covers high-throughput GPU serving.
- Install Ollama if missing, then ollama pull qwen3.5:0.8b.
- Verify with curl http://localhost:11434/v1/models.
- Register the endpoint: penguin config model add ... --client-type openai-chat --base-url http://localhost:11434/v1 — a pulled Ollama model…
- Confirm the new entry with penguin config model list.
What it can do on your machine
Read from SKILL.md and the folder at commit d56d9ce. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
ollamacurlsh
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
ollama.com
Also links to:
huggingface.co
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Ollama loads about 839 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 305 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePipes a well-known installer script into a shellSKILL.md:37
curl -fsSL https://ollama.com/install.sh | sh # Linux; macOS/Windows use the desktop app
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from Prism-Shadow/penguin-harness at commit d56d9ce, republished under its Apache-2.0 licence (© Prism-Shadow). 305 words, ~839 tokens.
Download SKILL.mdSave it as
.claude/skills/ollama/SKILL.md (or your agent's skills folder).- name
- ollama
- description
- Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
Ollama Serving
Ollama runs open-weight models locally with automatic GPU detection and an OpenAI-compatible API on http://localhost:11434.
Before you start
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.
Suggested workflow
- Ask the user which model to run; with no preference, recommend Qwen/Qwen3.5-0.8B (
qwen3.5:0.8b). - Pick the engine the user prefers: Ollama is the default; vLLM covers high-throughput GPU serving.
- Install Ollama if missing, then
ollama pull qwen3.5:0.8b. - Verify with
curl http://localhost:11434/v1/models. - Register the endpoint:
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. - Confirm the new entry with
penguin config model list.
Install
curl -fsSL https://ollama.com/install.sh | sh # Linux; macOS/Windows use the desktop appThe service then listens on http://localhost:11434.
Pull and run
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 modelOpenAI-compatible endpoint
The endpoint is http://localhost:11434/v1; any non-empty API key is accepted (conventionally ollama):
curl http://localhost:11434/v1/modelsContext length
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 32768ollama create qwen3.5-32k -f ModelfileRegister with PenguinHarness
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© Prism-Shadow, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in plugins/model-development/skills/ollama of Prism-Shadow/penguin-harness.
Open the folder on GitHubat commit d56d9ce
Compare with similar skills
Ollama next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ollama this skillPrism-Shadow/penguin-harness | 2.5k | — | ~839 | Automated safety check: Notes | Apache-2.0 | |
| Page AgentTommy-yw/RunbookHermes | 546 | 3 repos | ~2.3k | Automated safety check: Notes | MIT | |
| Aider DelegateamElnagdy/delegate-skills | 2.3k | 2 repos | ~3k | Automated safety check: Pass | MIT | |
| Perfupraullenchai/Rapid-MLX | 3.9k | — | ~1.6k | Automated safety check: Notes | Custom licence | |
| Ideer Daily Paper ChatbotAI45Lab/iDeer | 416 | — | ~3k | Automated safety check: Notes | AGPL-3.0 | |
| Agent Frameworkjihadkhawaja/Egroo | 178 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 |
Similar skills
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amElnagdy/delegate-skills
Delegate a coding task to Aider (aider) as a background implementer, then review its diff and land it yourself.
Perfup
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
Ideer Daily Paper Chatbot
AI45Lab/iDeer
Use iDeer as a daily paper-reading workflow for chatbot-first users such as Codex, Gemini, or ChatGPT.
Agent Framework
jihadkhawaja/Egroo
Build, extend, and debug AI agents in Egroo using the Microsoft Agent Framework (C .NET).
Resolve
alexziskind1/model-shelf
Always resolve Hugging Face models via model-shelf before any download.
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Categories
Questions about Ollama
What does Ollama do?
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents. Ollama is an agent skill from Prism-Shadow/penguin-harness. Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
When should I use Ollama?
Ollama fits situations like: tasks that involve LLM inference and serving.
How do I install Ollama in Claude Code?
Run `npx skills add Prism-Shadow/penguin-harness --skill ollama -a claude-code`. Or copy the skill folder (plugins/model-development/skills/ollama in Prism-Shadow/penguin-harness) into .claude/skills/ollama in your project. Claude Code loads it when a task matches its description.
How do I install Ollama in Codex?
Run `npx skills add Prism-Shadow/penguin-harness --skill ollama -a codex`. Or copy the skill folder (plugins/model-development/skills/ollama in Prism-Shadow/penguin-harness) into .agents/skills/ollama in your project. Codex loads it when a task matches its description.
Can I use Ollama in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prism-Shadow/penguin-harness --skill ollama -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ollama, .gemini/skills/ollama, .github/skills/ollama and .opencode/skills/ollama in your project.
What does Ollama need to run?
Going by SKILL.md and its folder, Ollama needs the command-line tools its instructions call (ollama, curl and sh).
Does Ollama access the network?
SKILL.md names 2 domains. In commands or code: ollama.com; the agent is likely to contact it when it follows the instructions. As links in the text: huggingface.co. This is read from the text; nothing was executed.
Is Ollama safe to install?
Our automated static check of SKILL.md found notes only (pipes a well-known installer script into a shell), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Ollama use?
Ollama is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Ollama use?
About 839 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Ollama?
Skills that share tags, products or a category with Ollama: Page Agent (Tommy-yw/RunbookHermes, 546 stars), Aider Delegate (amElnagdy/delegate-skills, 2.3k stars), Perfup (raullenchai/Rapid-MLX, 3.9k stars) and Ideer Daily Paper Chatbot (AI45Lab/iDeer, 416 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ollama?
Prism-Shadow (a GitHub organization) maintains it in Prism-Shadow/penguin-harness, which has 2,464 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 7, 2026.
Source: Prism-Shadow/penguin-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.