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GEO INFER TEST

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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.

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  • Added September 1, 2026
developmentpythongobashtestingapici/cdperformance

Works with

  • cli
  • api

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Scanned September 22, 2026

npx -y skills add ActiveInferenceInstitute/GEO-INFER --skill GEO-INFER-TEST --agent claude-code

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SKILL.md
---
name: geo-infer-test
description: 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.
prerequisites:
  required: []
  recommended: []
difficulty: beginner
estimated_time: 30min
examples_dir: ../GEO-INFER-EXAMPLES/examples/
---

# GEO-INFER-TEST

## Instructions

### Core Capabilities

- **Unified test runner**: `run_unified_tests.py` for all 45 modules
- **Category filtering**: unit, integration, system, performance
- **Module filtering**: Test individual or groups of modules
- **Result reporting**: JUnit XML + HTML reports per module
- **Fixtures**: Shared spatial test data and coordinate generators

### Usage

```bash
# Run all tests
uv run python GEO-INFER-TEST/run_unified_tests.py

# Run specific module
uv run python GEO-INFER-TEST/run_unified_tests.py --module MATH

# Run by category
uv run python GEO-INFER-TEST/run_unified_tests.py --category integration

# Canonical runner
uv run python GEO-INFER-TEST/run_unified_tests.py --module MATH
```

### Pytest Markers

`unit`, `integration`, `system`, `performance`, `geospatial`, `api`, `slow`, `fast`

## Examples

```python
# Spatial test fixture: generate realistic test coordinates
import numpy as np

def make_test_coordinates(n=100, center=(45.5, -122.6), spread=0.1):
    """Generate n random lat/lng pairs around a center point."""
    lats = np.random.normal(center[0], spread, n)
    lngs = np.random.normal(center[1], spread, n)
    return list(zip(lats.clip(-90, 90), lngs.clip(-180, 180)))

coords = make_test_coordinates(200)
```

```python
# Property-based test example with Hypothesis
from hypothesis import given, strategies as st
import numpy as np

@given(
    lat=st.floats(min_value=-90, max_value=90),
    lng=st.floats(min_value=-180, max_value=180),
    resolution=st.integers(min_value=0, max_value=15)
)
def test_h3_roundtrip(lat, lng, resolution):
    """H3 cell encoding/decoding preserves approximate coordinates."""
    import h3
    cell = h3.latlng_to_cell(lat, lng, resolution)
    rlat, rlng = h3.cell_to_latlng(cell)
    assert abs(rlat - lat) < 1.0  # Within ~1 degree
```

```bash
# Run specific test categories
uv run python GEO-INFER-TEST/run_unified_tests.py --module MATH
uv run python GEO-INFER-TEST/run_unified_tests.py --module SPACE
uv run python GEO-INFER-TEST/run_unified_tests.py --module ACT
```

## Guidelines

- Use real objects in tests
- Property-based tests (Hypothesis) in ≥10 modules
- Results saved to `.geo-infer-test-results/{MODULE}_results.xml`

### Integrations

- **EXAMPLES** → Example code treated as integration tests
- **OPS** → CI/CD test results feed monitoring
- **All modules** → Unified test runner spans all 45 modules

Files in this skill

  • AGENTS.md2.4 KB
  • README.md3.3 KB
  • SKILL.md2.8 KB
  • TESTING.md3.6 KB
  • build_package_wheels.py5.4 KB
  • config/AGENTS.md1.2 KB
  • config/README.md974 B
  • config/example.yaml2.4 KB
  • demo/README.md1.5 KB
  • demo/__init__.py75 B
  • demo/crescent_city_civic_intel_demo.py28 KB
  • demo/test_crescent_city_civic_intel_demo.py8.1 KB
  • docs/api_reference.md2.9 KB
  • docs/examples/advanced_example.md1.3 KB
  • docs/examples/basic_example.md1.3 KB
  • docs/getting_started.md2.5 KB
  • docs/index.md3.8 KB
  • examples/AGENTS.md1.2 KB
  • examples/README.md1.1 KB
  • examples/basic_testing_example.py8.1 KB

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