geo-infer-agent
Multi-agent geospatial systems with Active Inference. Use when building spatial agents, implementing perception-action loops, managing agent telemetry, or coordinating multi-agent spatial exploration.
Best use case
geo-infer-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multi-agent geospatial systems with Active Inference. Use when building spatial agents, implementing perception-action loops, managing agent telemetry, or coordinating multi-agent spatial exploration.
Teams using geo-infer-agent 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-AGENT/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-agent Compares
| Feature / Agent | geo-infer-agent | 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?
Multi-agent geospatial systems with Active Inference. Use when building spatial agents, implementing perception-action loops, managing agent telemetry, or coordinating multi-agent spatial exploration.
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-AGENT
## Instructions
### Core Capabilities
- **Agent base**: Transport pattern for agent communication
- **Active Inference**: Agent-level free energy minimization
- **Telemetry**: JSON snapshot + per-agent metrics tracking
- **Coordination**: Message passing, shared beliefs, swarm protocols
- **Rule-based**: Decision-tree agents for simple spatial tasks
### Key Imports
```python
from geo_infer_agent.core.agent_base import GeoAgent
from geo_infer_agent.core.active_inference import ActiveInferenceAgent
from geo_infer_agent.core.telemetry import AgentTelemetry
from geo_infer_agent.models.rule_based import RuleBasedAgent
```
## Examples
```python
from geo_infer_agent.core.agent_base import GeoAgent
from geo_infer_agent.core.telemetry import AgentTelemetry
agent = GeoAgent(agent_id="explorer_01", position=(45.5, -122.6))
telemetry = AgentTelemetry(agent)
# Perception-action loop
observation = agent.perceive(environment_state)
action = agent.decide(observation)
agent.act(action)
# Snapshot telemetry as JSON
snapshot = telemetry.snapshot()
print(f"Steps: {snapshot['total_steps']}, Position: {snapshot['position']}")
```
```python
from geo_infer_agent.core.active_inference import ActiveInferenceAgent
import numpy as np
ai_agent = ActiveInferenceAgent(n_states=8, n_observations=5, n_actions=4)
obs = np.random.dirichlet(np.ones(5))
action = ai_agent.act(obs)
print(f"Selected action: {action}, Free energy: {ai_agent.free_energy:.4f}")
```
## Guidelines
- Telemetry uses JSON snapshots and per-agent metrics (not TODOs)
- Agent base uses transport pattern for communication
- Test: `uv run python -m pytest GEO-INFER-AGENT/tests/ -v`
### Integrations
- **ACT** → Active Inference agent decision-making
- **SIM** → Multi-agent simulation environments
- **ANT** → Swarm intelligence coordination
- **SPACE** → Spatial state representation for agents
- **APP** → Agent configuration and control widgets
- **OPS** → Agent telemetry monitoring