geo-infer-energy
Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects.
Best use case
geo-infer-energy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects.
Teams using geo-infer-energy 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-ENERGY/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-energy Compares
| Feature / Agent | geo-infer-energy | 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?
Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects.
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-ENERGY
## Instructions
### Core Capabilities
- **LCOE**: Levelized cost of energy calculations for solar, wind, hydro
- **Renewable siting**: Resource assessment, terrain analysis, constraint mapping
- **Grid analysis**: Spatial energy grid modeling, load flow, transmission losses
- **Techno-economics**: NPV, IRR, payback analysis for energy investments
- **Emissions**: Carbon intensity mapping, reduction pathway scenarios
### Key Imports
```python
from geo_infer_energy.core.lcoe import LCOECalculator
from geo_infer_energy.core.renewable_siting import RenewableSiteSelector
from geo_infer_energy.core.grid_analysis import GridAnalyzer
from geo_infer_energy.core.techno_economics import TechnoEconomicModel
```
## Examples
```python
from geo_infer_energy.core.renewable_siting import RenewableSiteSelector
selector = RenewableSiteSelector(technology="solar")
candidates = selector.evaluate(
solar_irradiance=ghi_raster,
terrain=dem,
constraints={"slope_max": 15, "distance_from_grid_km": 10}
)
optimal_sites = selector.rank(candidates, n_top=5)
```
## Guidelines
- LCOE benchmarking in development (Alpha)
### Integrations
- Integrates with CLIMATE for renewable resource projections
- Integrates with SPACE for spatial optimization grid
- Test: `uv run python -m pytest GEO-INFER-ENERGY/tests/ -v`