geo-infer-examples
Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities.
Best use case
geo-infer-examples is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities.
Teams using geo-infer-examples 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-EXAMPLES/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-examples Compares
| Feature / Agent | geo-infer-examples | 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?
Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities.
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-EXAMPLES
## Instructions
### Core Capabilities
- **Module orchestrators**: Cross-module workflow examples
- **Domain examples**: Per-domain analysis workflows
- **Active Inference**: End-to-end inference pipeline examples
- **Integration patterns**: How modules connect and compose
### Key Directories
```text
GEO-INFER-EXAMPLES/examples/
├── module_orchestrator.py # Cross-module coordination
├── spatial_analysis/ # SPACE + MATH examples
├── active_inference/ # ACT + BAYES examples
└── domain/ # Domain-specific examples
```
### Orchestrator Example
```python
from geo_infer_examples.module_orchestrator import ModuleOrchestrator
orchestrator = ModuleOrchestrator(modules=["SPACE", "MATH", "BAYES"])
result = orchestrator.run(data, max_iterations=100)
# Convergence uses numeric relative-change (not placeholder)
```
## Examples
```python
from geo_infer_examples.module_orchestrator import ModuleOrchestrator
# Cross-module pipeline: SPACE → MATH → BAYES
orchestrator = ModuleOrchestrator(modules=["SPACE", "MATH", "BAYES"])
result = orchestrator.run(data, max_iterations=100)
# Convergence uses numeric relative-change (not placeholder)
print(f"Converged in {result.iterations} iterations")
```
```python
# Domain workflow: Agricultural risk assessment
from geo_infer_ag.models.soil_health import SoilHealthModel
from geo_infer_risk.core.risk_engine import RiskEngine
from geo_infer_space.backends.h3 import H3Backend
# 1. Tessellate farm boundary
cells = H3Backend().tessellate(farm_polygon, resolution=9)
# 2. Assess soil health per cell
soil = SoilHealthModel()
health_scores = {cell: soil.assess(cell) for cell in cells}
# 3. Compute risk
risk = RiskEngine()
risk_map = risk.assess(hazard=drought_index, exposure=health_scores)
```
## Guidelines
- `module_orchestrator.py` convergence check uses numeric relative-change
- Missing end-to-end Active Inference tutorial (planned for v0.4.0)
- Test: `uv run python -m pytest GEO-INFER-EXAMPLES/tests/ -v`
### Integrations
- **INTRA** → Documentation hub links to examples
- **All modules** → Examples demonstrate cross-module workflows
- **TEST** → Example code testable as integration tests