geo-infer-ant
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
Best use case
geo-infer-ant is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
Teams using geo-infer-ant 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-ANT/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-ant Compares
| Feature / Agent | geo-infer-ant | 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?
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
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-ANT
## Instructions
### Core Capabilities
- **ACO**: Ant Colony Optimization for spatial routing and TSP
- **PSO**: Particle Swarm Optimization for continuous spatial problems
- **ABC**: Artificial Bee Colony for facility location optimization
- **Stigmergy**: Pheromone-based coordination on spatial grids
- **Colony convergence**: Iteration tracking, solution quality metrics
### Key Imports
```python
from geo_infer_ant.core.aco import AntColonyOptimizer
from geo_infer_ant.core.pso import ParticleSwarmOptimizer
from geo_infer_ant.core.abc import ArtificialBeeColony
from geo_infer_ant.core.pheromone import PheromoneGrid
```
## Examples
```python
from geo_infer_ant.core.aco import AntColonyOptimizer
optimizer = AntColonyOptimizer(
n_ants=50, alpha=1.0, beta=2.0, rho=0.5
)
best_route = optimizer.solve(distance_matrix, n_iterations=100)
print(f"Best route cost: {best_route.cost}")
```
## Guidelines
- Tests have long runtime (~213s) due to convergence iterations
- Convergence verification in development (Alpha)
### Integrations
- Integrates with AGENT for multi-agent swarm coordination
- Test: `uv run python -m pytest GEO-INFER-ANT/tests/ -v`