ollama-local-llm-runner-and-model-server
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Manual integration
- External API key
- Not required
- Operating systems
- Docker
- Runtime requirements
- Python · Docker
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: ollama-local-llm-runner-and-model-server
description: Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds o…
category: ai
runtime: Python / Docker
---
# ollama-local-llm-runner-and-model-server output preview
## PART A: Task fit
- Use case: Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds of open models including Llama, Gemma, Qwen, and DeepSeek, with GPU acceleration and an OpenAI-compatible API endpoint. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Installation / Source” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds of open models including Llama, Gemma, Qwen, and DeepSeek, with GPU acceleration and an OpenAI-compatible API endpoint. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Installation / Source” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Installation / Source”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: ollama-local-llm-runner-and-model-server
description: Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds o…
category: ai
source: tomevault-io/skills-registry
---
# ollama-local-llm-runner-and-model-server
## When to use
- Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds of open models includin…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Installation / Source” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "ollama-local-llm-runner-and-model-server" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Installation / Source
rules -> SKILL.md triggers / order / output contract
runtime -> Python / Docker | read files, write/modify files | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Ollama Local LLM Runner and Model Server
Ollama runs large language models locally with a simple CLI and REST API. It supports hundreds of open models including Llama, Gemma, Qwen, and DeepSeek, with GPU acceleration and an OpenAI-compatible API endpoint.
Installation
Use the upstream install or setup path that matches your environment:
- pip install ollama
- npm i ollama
Requirements and caveats from upstream:
Docker
- The official Ollama Docker image ollama/ollama is available on Docker Hub.
- ollama-python
Basic usage or getting-started notes:
You'll be prompted to run a model or connect Ollama to your existing agents or applications such as Claude Code, OpenClaw, OpenCode , Codex, Copilot, and more.
Run and chat with Gemma 3:
ollama run gemma3
Source: https://github.com/ollama/ollama
Extracted from upstream docs: https://raw.githubusercontent.com/ollama/ollama/HEAD/README.md
Source
Source: agentskillexchange/skills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review