geo-infer-test
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.
Best use case
geo-infer-test is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using geo-infer-test 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-TEST/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-test Compares
| Feature / Agent | geo-infer-test | 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?
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.
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-TEST
## Instructions
### Core Capabilities
- **Unified test runner**: `run_unified_tests.py` for all 44 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
# Direct pytest
uv run python -m pytest GEO-INFER-MATH/tests/unit/ -v --tb=short
```
### 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 -m pytest GEO-INFER-MATH/tests/ -m "unit and not slow" -v
uv run python -m pytest GEO-INFER-SPACE/tests/ -m "geospatial" -v
uv run python -m pytest GEO-INFER-ACT/tests/ -m "integration" -v --tb=short
```
## Guidelines
- Zero mock implementations in tests — use real objects
- Property-based tests (Hypothesis) in ≥10 modules
- Results saved to `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 44 modules