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Central documentation hub and cross-module integration guides for GEO-INFER. Use when navigating documentation, finding cross-module integration patterns, understanding data flow architecture, or locating tutorials and API references.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
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.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.
Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.
| name | geo-infer-intra |
| description | Central documentation hub and cross-module integration guides for GEO-INFER. Use when navigating documentation, finding cross-module integration patterns, understanding data flow architecture, or locating tutorials and API references. |
| prerequisites | {"required":[],"recommended":[]} |
| difficulty | beginner |
| estimated_time | 30min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
docs/ directory with comprehensive guidesGEO-INFER-INTRA/docs/
├── guides/ # How-to guides for common workflows
├── tutorials/ # Step-by-step multi-module tutorials
├── integration/ # Cross-module integration patterns
├── architecture/ # System design and data flow diagrams
└── api/ # Consolidated API reference
# Example: SPACE → MATH → BAYES pipeline
from geo_infer_space.backends.h3 import H3Backend
from geo_infer_math.core.spatial_statistics import MoranI
from geo_infer_bayes.models.spatial_gp import SpatialGP
# 1. Index → 2. Analyze → 3. Model
cells = H3Backend().tessellate(region, resolution=7)
moran = MoranI(weights)
autocorrelation = moran.compute(values)
model = SpatialGP()
# Multi-module data flow: DATA → SPACE → MATH → BAYES
from geo_infer_data.connectors.file import FileConnector
from geo_infer_space.backends.h3 import H3Backend
from geo_infer_math.core.spatial_statistics import MoranI
from geo_infer_bayes.models.spatial_gp import SpatialGP
# 1. Load → 2. Index → 3. Analyze → 4. Model
connector = FileConnector(base_path="data")
# In an async workflow: features = await connector.read_geospatial("observations.geojson")
cells = H3Backend().tessellate(region, resolution=7)
moran = MoranI(weights)
autocorrelation = moran.compute(values)
model = SpatialGP()
# Onboarding: discover module capabilities
import importlib
for module_name in ["math", "space", "bayes", "act", "risk"]:
mod = importlib.import_module(f"geo_infer_{module_name}")
print(f"geo_infer_{module_name}: {mod.__doc__ or 'No docstring'}")
uv run python -m pytest GEO-INFER-INTRA/tests/ -v