A professional toolset for running, tuning, and troubleshooting local Ollama models with reliable API patterns and hardware-aware workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ollama
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ollama using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Ollama?
The Ollama skill is a specialized extension for Openclaw Skills designed to streamline the lifecycle of local Large Language Models. It provides developers with a structured framework to install, configure, and optimize Ollama runtimes across macOS, Linux, and Windows. By focusing on practical execution rather than abstract discussion, this skill ensures that local AI deployments are stable, reproducible, and performant.
Central to this skill is the ability to manage Modelfiles, handle embeddings for RAG pipelines, and navigate the nuances of GPU versus CPU inference. It bridges the gap between raw CLI commands and sophisticated agentic workflows, allowing for precise control over model tags, context windows, and API interactions while maintaining a durable local memory of the host environment.
Ollama Use Cases
- Automating the setup and smoke-testing of local AI runtimes on developer machines.
- Tuning model performance and context sizing to fit specific VRAM and hardware constraints.
- Standardizing Modelfiles to ensure consistent system prompts and parameters across different local models.
- Implementing reliable local RAG systems by aligning embedding models with retrieval logic.
- Debugging API connectivity and ensuring structured JSON outputs for application integrations.
How Ollama Works
- Runtime Validation: The skill starts by verifying the ollama installation and checking for active service status using live commands.
- Environment Mapping: It analyzes the host hardware, including GPU class and memory availability, to establish performance baselines.
- Structured Memory Initialization: A dedicated directory at ~/ollama/ is utilized to store a model registry, environment facts, and incident logs.
- Model Orchestration: Exact model tags are pulled and inspected to ensure the correct quantization and capabilities are available for the task.
- Workflow Execution: The agent applies optimized API patterns or Modelfile configurations based on the specific developer requirement.
- Performance Monitoring: It uses real-time telemetry to detect CPU fallback or memory pressure and adjusts advice accordingly.
Ollama Setup
To integrate this skill into your Openclaw Skills workflow, ensure the Ollama binary is available on your system path and follow these steps:
# Install the Ollama skill via clawhub
clawhub install ollama
# Verify your local environment
ollama --version
# Initialize the skill memory structure
# This creates the ~/ollama/ directory and operational logs
Ollama Data Schema & Taxonomy
The skill organizes critical operational context within the ~/ollama/ directory to ensure consistent behavior across Openclaw Skills sessions:
| Component | File | Purpose |
|---|---|---|
| Memory | memory.md |
Stores durable context and activation boundaries. |
| Environment | environment.md |
Tracks OS, GPU class, and runtime service details. |
| Registry | model-registry.md |
Catalogs approved tags, quants, and fit notes. |
| Templates | modelfiles.md |
Maintains reusable Modelfile patterns and parameters. |
| RAG Config | rag-notes.md |
Documents embedding choices and vector dimensions. |
| Incidents | incident-log.md |
Logs repeated failures and successful recovery paths. |
Ollama Advanced Features
- Hardware-aware deployment logic that prevents inefficient memory swapping or CPU fallback.
- Support for both native Ollama REST APIs and OpenAI-compatible v1 endpoints.
- Deterministic JSON output enforcement for strict data extraction workflows.
- Safe remote access protocols to guide the expansion of local services without security regressions.
- Automated RAG fit checks to verify consistency between indexing and querying models.
SKILL.md
Loading
Related Openclaw Skills
Giggle Video Generation
A powerful AI video generation skill supporting text and image-to-video workflows across multiple state-of-the-art models like Sora 2, Grok, and Kling.
OpenClaw Daily News
An automated news aggregation and analysis tool designed to monitor the OpenClaw open-source AI agent ecosystem.
Cloudflare Browser Rendering
A specialized tool for extracting rendered Markdown from JavaScript-heavy webpages and performing asynchronous site-wide crawls via Cloudflare APIs.
X Growth Automation
A modular and reusable framework for building autonomous X/Twitter growth systems with built-in safety guardrails and multi-source content integration.
SiliconFlow Qwen2.5-VL Vision
A high-performance visual reasoning skill utilizing the Qwen2.5-VL model through SiliconFlow for advanced image analysis and description.
Claw Messenger
A high-performance messaging plugin that bridges OpenClaw agents to iMessage, RCS, and SMS networks without requiring dedicated hardware.