geo-infer-sim
Agent-based simulation for geospatial environments. Use when building spatial simulations, modeling agent interactions in geographic space, running Monte Carlo spatial experiments, or comparing spatial planning scenarios.
Best use case
geo-infer-sim is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Agent-based simulation for geospatial environments. Use when building spatial simulations, modeling agent interactions in geographic space, running Monte Carlo spatial experiments, or comparing spatial planning scenarios.
Teams using geo-infer-sim should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-SIM/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-sim Compares
| Feature / Agent | geo-infer-sim | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Agent-based simulation for geospatial environments. Use when building spatial simulations, modeling agent interactions in geographic space, running Monte Carlo spatial experiments, or comparing spatial planning scenarios.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-SIM
## Instructions
### Core Capabilities
- **Agent-based modeling**: Spatial agents on grids, networks, and continuous space
- **Environment simulation**: Geographic environment state management, land use dynamics
- **Monte Carlo**: Stochastic spatial experiments with ensemble statistics
- **Scenario analysis**: What-if spatial scenario comparison and sensitivity analysis
- **Visualization**: Simulation playback, spatial animation, time-step rendering
### Key Imports
```python
from geo_infer_sim.core.simulation import SpatialSimulation
from geo_infer_sim.core.environment import GeoEnvironment
from geo_infer_sim.core.scenario import ScenarioManager
from geo_infer_sim.core.monte_carlo import MonteCarloRunner
```
## Examples
```python
from geo_infer_sim.core.simulation import SpatialSimulation
sim = SpatialSimulation(
grid_size=(100, 100),
n_agents=50,
time_steps=200
)
sim.add_rule("diffusion", rate=0.1)
results = sim.run()
final_state = results.get_snapshot(t=200)
```
## Guidelines
- Mesa integration in development (Alpha)
### Integrations
- Integrates with AGENT for Active Inference agent behavior
- Integrates with ANT for swarm simulation
- Test: `uv run python -m pytest GEO-INFER-SIM/tests/ -v`