Outside Agent: a local-first prompt that gets the screen out of the way

This is a draft submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass. It is deliberately marked as a draft: the CLI and its tests are complete, but I have not yet run a locally installed model or published the source repository.

Outside Agent is a small command-line tool that turns a few constraints into one outdoor micro-adventure: a broad area, available time, energy level, and an interest such as birds, plants, or light.

The point is to make the screen the shortest part of the experience. The model makes a compact plan; the person then leaves the device behind and completes the activity outside.

The tool intentionally asks only for a broad area. It does not collect an address, track location, check weather, invent trail conditions, or make claims about opening hours or accessibility. Every generated plan tells the user to choose a public, legal, familiar place and verify local conditions themselves.

The implemented CLI accepts a local Ollama model and a small input set:

python3 outside_agent.py \ --area "a nearby public park" \ --minutes 35 \ --energy low \ --interest birds Enter fullscreen mode Exit fullscreen mode It sends one request to http://127.0.0.1:11434/api/generate with streaming disabled, then prints the model's Markdown plan.

The project has automated tests for the user-context prompt, its safety contract, and the local Ollama request shape:

Ran 3 tests in 0.000s OK Enter fullscreen mode Exit fullscreen mode I have not run a downloaded model in this environment yet, so I am not presenting a generated outdoor plan as a measured result. A real local-model transcript or video demo is the next required validation before this draft becomes a contest submission.

The initial source is a standard-library Python CLI with a small unit-test suite:

Before publication, I will make this source available in a public repository and add the repository URL here. I will not imply that a private local folder is a public code release.

The open component is the model runtime: Outside Agent uses Ollama to call a locally installed open-weight model. The default model name is gemma3:4b, but the command accepts any installed model name through --model.

The model is constrained by a system prompt that requires four sections:

The prompt also forbids local factual claims and medical, emergency, wildlife-handling, or navigation advice. Those are deliberately human decisions, not model outputs.

For this project, local inference is not a decorative implementation detail. It makes the tool usable without a cloud API key and lets a person choose the model that runs on their own machine. The user controls the model, the runtime, and the small amount of context that leaves the command line.

That is a better fit for a tool whose purpose is to reduce time on a screen: it should not require a new account, an analytics pipeline, or continuous location collection in order to suggest a short walk, a bird-listening loop, or a plant-observation prompt.

Open-weight models also make the prompt and safety boundary inspectable. If a community finds a better way to make the planner more useful or more cautious, the implementation is small enough to understand and change.

This draft is not a claim that the model has been evaluated. Before submitting it to the challenge, I will: