geo-infer-ag
Precision agriculture and soil health modeling. Use when analyzing soil health, crop water usage (FAO-56), carbon sequestration (IPCC Tier 1), precision farming, or agricultural land management.
Best use case
geo-infer-ag is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Precision agriculture and soil health modeling. Use when analyzing soil health, crop water usage (FAO-56), carbon sequestration (IPCC Tier 1), precision farming, or agricultural land management.
Teams using geo-infer-ag 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-AG/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-ag Compares
| Feature / Agent | geo-infer-ag | 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?
Precision agriculture and soil health modeling. Use when analyzing soil health, crop water usage (FAO-56), carbon sequestration (IPCC Tier 1), precision farming, or agricultural land management.
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-AG
## Instructions
### Core Capabilities
- **Soil health**: USDA soil data integration, nutrient modeling
- **Carbon sequestration**: IPCC Tier 1 methodology for agricultural carbon
- **Water usage**: FAO-56 crop-specific water productivity calculations
- **Precision farming**: Variable rate application, yield prediction
- **Land management**: Crop rotation optimization, field boundary analysis
### Key Imports
```python
from geo_infer_ag.models.soil_health import SoilHealthModel
from geo_infer_ag.models.carbon_sequestration import CarbonSequestrationModel
from geo_infer_ag.models.water_usage import WaterUsageModel
```
## Examples
```python
from geo_infer_ag.models.water_usage import WaterUsageModel
model = WaterUsageModel(crop="maize", climate_zone="temperate")
daily_water = model.compute_daily_requirement(
eto=5.2, # reference evapotranspiration (mm/day)
growth_stage="mid_season"
)
print(f"Crop water need: {daily_water:.1f} mm/day")
seasonal = model.seasonal_demand(planting_date="2026-04-15")
```
```python
from geo_infer_ag.models.carbon_sequestration import CarbonSequestrationModel
carbon = CarbonSequestrationModel(method="ipcc_tier1")
result = carbon.estimate(
land_area_ha=100,
soil_type="clay_loam",
management="no_till"
)
print(f"Annual sequestration: {result.tonnes_co2_per_year:.1f} t CO₂/yr")
```
## Guidelines
- Soil models use USDA data (not random values)
- Carbon uses IPCC Tier 1 methodology
- Water productivity uses FAO-56 crop-specific defaults
- Test: `uv run python -m pytest GEO-INFER-AG/tests/ -v`
### Integrations
- **CLIMATE** → Precipitation projections for irrigation planning
- **WATER** → Irrigation water demand modeling
- **SPACE** → H3-based field tessellation and precision farming grids
- **RISK** → Crop loss risk assessment
- **ECON** → Agricultural market pricing and supply chain