Ollama
Ollama local LLM deployment and management. Use for running LLMs locally.
Install
$ agentstack add skill-g1joshi-agent-skills-ollama ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-ollama) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Ollama
Ollama makes running LLMs locally as easy as docker run. 2025 updates include Windows/AMD support, Multimodal input, and Tool Calling.
When to Use
- Local Development: Coding without wifi or API costs.
- Privacy: Processing sensitive documents on-device.
- Integration: Works with LangChain, LlamaIndex, and Obsidian natively.
Core Concepts
Modelfile
Docker-like file to define a custom model (System prompt + Base model).
FROM llama3
SYSTEM You are Mario from Super Mario Bros.API
Ollama runs a local server (localhost:11434) compatible with OpenAI SDK.
Best Practices (2025)
Do:
- Use high-speed RAM: Local LLM speed depends on memory bandwidth.
- Use Quantized Models:
q4_k_mis the sweet spot for speed/quality balance. - Unload:
ollama stopwhen done to free VRAM for games/rendering.
Don't:
- Don't expect GPT-4 level: Smaller local models (8B) are smart but lack deep reasoning.
References
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.