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.
Resources
6Install
npx skillscat add activeinferenceinstitute/geo-infer/geo-infer-intra Install via the SkillsCat registry.
About this skill
This skill provides centralized documentation and integration guides for GEO-INFER, enabling developers to navigate system architecture, understand data flow, and implement cross-module workflows. It solves the problem of fragmented documentation by consolidating API references, tutorials, and integration patterns. Use it when building multi-module pipelines or requiring clarity on system design and data handling.
SKILL.md
GEO-INFER-INTRA
Instructions
Core Capabilities
- Documentation hub: Central
docs/directory with comprehensive guides - Integration guides: Cross-module data flow patterns and examples
- Architecture docs: System design diagrams, module dependency graph
- API reference: Consolidated API documentation for all 44 modules
- Tutorials: Step-by-step workflows spanning multiple modules
Key Directories
GEO-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 referenceCross-Module Integration Pattern
# 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()Examples
# 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'}")Guidelines
- Start here when onboarding to GEO-INFER
- Each module's README.md and AGENTS.md provide module-level detail
- Each module's SKILL.md provides quick-reference for Claude Code
- Test:
uv run python -m pytest GEO-INFER-INTRA/tests/ -v
Integrations
- EXAMPLES → Working code examples referenced from docs
- All modules → Central hub linking all module documentation
- TEST → Documentation-driven testing patterns