# Biosample Enricher
Infer AI-friendly environmental and geographic metadata about biosamples from multiple sources.
[](https://python.org)
[](https://opensource.org/licenses/MIT)
[](https://github.com/astral-sh/ruff)
[](https://mypy-lang.org/)
## Overview
Biosample Enricher provides 8 specialized services for enriching biosample metadata with environmental and geographic information from authoritative data sources. Each service focuses on a specific domain (elevation, weather, soil, marine, land cover, geocoding, geographic features) and returns structured, type-safe data ready for analysis or AI applications.
## Features
- **8 Specialized Services**: Elevation, soil, weather, marine, land cover, forward/reverse geocoding, geographic features
- **Service-Based Architecture**: Independent services with focused responsibilities
- **Type Safety**: Full type hints with Pydantic validation and mypy checking
- **Smart Caching**: HTTP caching with coordinate canonicalization for efficiency
- **Multiple Providers**: Automatic fallback between data providers (USGS, Google, OSM, etc.)
- **Click-Based CLIs**: User-friendly command-line tools for each service
- **Flexible Installation**: Core services only, or add optional mongodb/metrics/schema extras
## Installation
### Prerequisites
- Python 3.11 or higher
- [UV package manager](https://github.com/astral-sh/uv) (recommended)
### Add to Your Project (Recommended)
```bash
# Basic installation - all 8 enrichment services
uv add biosample-enricher
# With optional dependencies
uv add biosample-enricher --extra metrics # Metrics and visualization
uv add biosample-enricher --extra mongodb # MongoDB support for NMDC/GOLD
uv add biosample-enricher --extra schema # Schema analysis tools
uv add biosample-enricher --extra all # All optional features
```
### From Source (Development)
```bash
# Clone and install
git clone https://github.com/contextualizer-ai/biosample-enricher.git
cd biosample-enricher
uv sync
# With optional extras
uv sync --extra mongodb # MongoDB support
uv sync --extra metrics # Metrics and visualization
uv sync --extra schema # Schema analysis tools
uv sync --extra all # Everything
```
## Quick Start
### Python API
The package exports 8 services from the top level:
```python
from biosample_enricher import (
ElevationService,
ElevationRequest,
SoilService,
WeatherService,
MarineService,
LandService,
ReverseGeocodingService,
ForwardGeocodingService,
OSMFeaturesService,
)
from datetime import date
# Get elevation for a location
elevation_service = ElevationService()
request = ElevationRequest(latitude=40.7128, longitude=-74.0060)
observations = elevation_service.get_elevation(request)
for obs in observations:
if obs.value_numeric is not None:
print(f"{obs.provider.name}: {obs.value_numeric}m")
# Output:
# usgs_3dep: 13.15m
# google_elevation: 13.26m
# open_topo_data: 25.0m
# osm_elevation: 51.0m
# Get weather data for a location and date
weather_service = WeatherService()
weather_result = weather_service.get_daily_weather(
lat=37.7749,
lon=-122.4194,
target_date=date(2024, 1, 15)
)
print(f"Temperature: {weather_result.temperature.value}°C")
print(f"Precipitation: {weather_result.precipitation.value}mm")
# Get soil properties
soil_service = SoilService()
soil_result = soil_service.enrich_location(
latitude=40.7128,
longitude=-74.0060,
depth_cm="0-5cm"
)
print(f"Provider: {soil_result.provider}")
print(f"Quality score: {soil_result.quality_score}")
# Get marine data (SST, bathymetry, chlorophyll)
marine_service = MarineService()
marine_result = marine_service.get_comprehensive_marine_data(
latitude=36.6,
longitude=-121.9,
target_date=date(2024, 1, 15)
)
if marine_result.sea_surface_temperature:
print(f"Sea surface temp: {marine_result.sea_surface_temperature.value}°C")
if marine_result.bathymetry:
print(f"Water depth: {marine_result.bathymetry.value}m")
# Reverse geocoding (coordinates -> place names)
geocoding_service = ReverseGeocodingService()
result = geocoding_service.reverse_geocode(lat=40.7128, lon=-74.0060)
if result:
print(f"Location: {result.get_formatted_address()}")
# Get nearby geographic features
osm_service = OSMFeaturesService()
features = osm_service.get_features_for_location(
latitude=37.7749,
longitude=-122.4194,
radius_m=1000
)
if features and features.named_features:
for feature in features.named_features[:5]:
print(f"{feature.name} ({feature.category}): {feature.distance_km:.2f}km")
```
### CLI Usage
Each service has its own CLI command:
```bash
# Elevation lookup
uv run elevation-lookup lookup --lat 40.7128 --lon -74.0060
# Soil data
uv run soil-enricher lookup --lat 40.7128 --lon -74.0060 --depth 10
# Weather data
uv run weather-enricher lookup --lat 37.7749 --lon -122.4194 --date 2024-01-15
# Marine data
uv run marine-enricher lookup --lat 36.6 --lon -121.9 --date 2024-01-15
# Land cover
uv run land-enricher lookup --lat 40.7128 --lon -74.0060
# Batch processing from CSV
uv run elevation-lookup batch --input samples.csv --lat-col latitude --lon-col longitude
# Version info
uv run biosample-version
```
## Services
### 1. Elevation Service
Get elevation data from multiple providers (USGS, Google, Open Topo Data).
**Providers**: USGS (US only, free), Google (global, requires API key), Open Topo Data (global, free)
**Python**:
```python
from biosample_enricher import ElevationService, ElevationRequest
service = ElevationService()
request = ElevationRequest(latitude=40.7128, longitude=-74.0060)
observations = service.get_elevation(request)
```
**CLI**:
```bash
uv run elevation-lookup lookup --lat 40.7128 --lon -74.0060
```
### 2. Soil Service
Get soil properties (texture, pH, organic carbon, etc.).
**Providers**: SoilGrids (global coverage), USDA NRCS (US only)
**Python**:
```python
from biosample_enricher import SoilService
service = SoilService()
soil_result = service.enrich_location(
latitude=40.7128,
longitude=-74.0060,
depth_cm="0-5cm"
)
```
**CLI**:
```bash
uv run soil-enricher lookup --lat 40.7128 --lon -74.0060 --depth 10
```
### 3. Weather Service
Get historical weather data (temperature, precipitation, humidity, etc.).
**Providers**: Open-Meteo (free, global), Meteostat (free, global)
**Python**:
```python
from biosample_enricher import WeatherService
from datetime import date
service = WeatherService()
weather_result = service.get_daily_weather(
lat=37.7749,
lon=-122.4194,
target_date=date(2024, 1, 15)
)
```
**CLI**:
```bash
uv run weather-enricher lookup --lat 37.7749 --lon -122.4194 --date 2024-01-15
```
### 4. Marine Service
Get marine data (sea surface temperature, bathymetry, chlorophyll).
**Providers**: NOAA OISST (SST), GEBCO (bathymetry), ESA CCI (chlorophyll)
**Python**:
```python
from biosample_enricher import MarineService
from datetime import date
service = MarineService()
marine_result = service.get_comprehensive_marine_data(
latitude=36.6,
longitude=-121.9,
target_date=date(2024, 1, 15)
)
```
**CLI**:
```bash
uv run marine-enricher lookup --lat 36.6 --lon -121.9 --date 2024-01-15
```
### 5. Land Service
Get land cover classification.
**Providers**: ESA WorldCover, MODIS, NLCD (US only)
**Python**:
```python
from biosample_enricher import LandService
service = LandService()
land_result = service.enrich_location(
latitude=40.7128,
longitude=-74.0060
)
```
**CLI**:
```bash
uv run land-enricher lookup --lat 40.7128 --lon -74.0060
```
### 6. Reverse Geocoding Service
Convert coordinates to human-readable addresses.
**Providers**: OSM Nominatim (free), Google Geocoding (requires API key)
**Python**:
```python
from biosample_enricher import ReverseGeocodingService
service = ReverseGeocodingService()
result = service.reverse_geocode(lat=40.7128, lon=-74.0060)
if result:
print(result.get_formatted_address())
```
### 7. Forward Geocoding Service
Convert addresses/place names to coordinates.
**Providers**: OSM Nominatim (free), Google Geocoding (requires API key)
**Python**:
```python
from biosample_enricher import ForwardGeocodingService
service = ForwardGeocodingService()
result = service.geocode("New York City")
if result and result.locations:
for location in result.locations[:3]:
print(f"{location.formatted_address}: {location.latitude}, {location.longitude}")
```
### 8. OSM Features Service
Get nearby geographic features (parks, water bodies, landmarks).
**Providers**: OpenStreetMap Overpass API (free), Google Places (requires API key)
**Python**:
```python
from biosample_enricher import OSMFeaturesService
service = OSMFeaturesService()
features = service.get_features_for_location(
latitude=37.7749,
longitude=-122.4194,
radius_m=1000
)
if features and features.named_features:
for feature in features.named_features[:5]:
print(f"{feature.name} ({feature.category})")
```
## API Keys
Only required for Google services (optional - OSM alternatives available for everything):
```bash
# Single API key for all Google services
export GOOGLE_MAIN_API_KEY="your-key-here"
```
All other services are free and require no authentication.
## Development
### Setup
```bash
# Clone repository
git clone https://github.com/contextualizer-ai/biosample-enricher.git
cd biosample-enricher
# Complete development setup
make dev-setup
```
### Testing
```bash
# Run fast tests (excludes network/slow tests)
make test-fast
# Run all tests with coverage
make test-cov
# Run specific test categories
make test-unit # Unit tests only
make test-integration # Integration tests
```
### Code Quality
```bash
# Format, lint, type-check, test
make dev-check
# Full CI validation
make check-ci
# Individual checks
make format # Format with ruff
make lint # Lint with ruff
make type-check # Type check with mypy
make dep-check # Check dependencies with deptry
```
## Project Structure
```
biosample-enricher/
├── biosample_enricher/
│ ├── __init__.py # Public API exports
│ ├── elevation/ # Elevation service
│ ├── soil/ # Soil service
│ ├── weather/ # Weather service
│ ├── marine/ # Marine service
│ ├── land/ # Land cover service
│ ├── reverse_geocoding/ # Reverse geocoding
│ ├── forward_geocoding/ # Forward geocoding
│ ├── osm_features/ # Geographic features
│ ├── models.py # Core data models
│ ├── http_cache.py # HTTP caching
│ └── cli*.py # CLI commands
├── tests/ # Test suite
├── pyproject.toml # Project configuration
└── Makefile # Development automation
```
## Dependencies
### Core Dependencies
- **Always installed**: pandas, rasterio, meteostat (required for weather aggregation and global soil coverage)
- CLI and data validation: click, pydantic, requests, rich, pyyaml
### Optional Dependencies
- **mongodb**: `pymongo` for fetching from NMDC/GOLD databases (evaluation/demo only)
- **metrics**: `matplotlib`, `seaborn` for visualization
- **schema**: `genson` for schema analysis
Install with: `uv sync --extra mongodb` or `uv sync --extra all`
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run checks (`make dev-check`)
5. Commit (`git commit -m 'Add amazing feature'`)
6. Push (`git push origin feature/amazing-feature`)
7. Open a Pull Request
See [CLAUDE.md](CLAUDE.md) for detailed development guidelines.
## License
MIT License - see [LICENSE](LICENSE) file for details.
## Acknowledgments
- Built with [UV](https://github.com/astral-sh/uv) for fast package management
- CLI powered by [Click](https://click.palletsprojects.com/)
- Data validation with [Pydantic](https://pydantic.dev/)
- Console output with [Rich](https://github.com/Textualize/rich)
- Caching with [requests-cache](https://github.com/requests-cache/requests-cache)
## Support
- **Issues**: [GitHub Issues](https://github.com/contextualizer-ai/biosample-enricher/issues)
- **Email**: info@contextualizer.ai
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"description": "# Biosample Enricher\n\nInfer AI-friendly environmental and geographic metadata about biosamples from multiple sources.\n\n[](https://python.org)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/astral-sh/ruff)\n[](https://mypy-lang.org/)\n\n## Overview\n\nBiosample Enricher provides 8 specialized services for enriching biosample metadata with environmental and geographic information from authoritative data sources. Each service focuses on a specific domain (elevation, weather, soil, marine, land cover, geocoding, geographic features) and returns structured, type-safe data ready for analysis or AI applications.\n\n## Features\n\n- **8 Specialized Services**: Elevation, soil, weather, marine, land cover, forward/reverse geocoding, geographic features\n- **Service-Based Architecture**: Independent services with focused responsibilities\n- **Type Safety**: Full type hints with Pydantic validation and mypy checking\n- **Smart Caching**: HTTP caching with coordinate canonicalization for efficiency\n- **Multiple Providers**: Automatic fallback between data providers (USGS, Google, OSM, etc.)\n- **Click-Based CLIs**: User-friendly command-line tools for each service\n- **Flexible Installation**: Core services only, or add optional mongodb/metrics/schema extras\n\n## Installation\n\n### Prerequisites\n\n- Python 3.11 or higher\n- [UV package manager](https://github.com/astral-sh/uv) (recommended)\n\n### Add to Your Project (Recommended)\n\n```bash\n# Basic installation - all 8 enrichment services\nuv add biosample-enricher\n\n# With optional dependencies\nuv add biosample-enricher --extra metrics # Metrics and visualization\nuv add biosample-enricher --extra mongodb # MongoDB support for NMDC/GOLD\nuv add biosample-enricher --extra schema # Schema analysis tools\nuv add biosample-enricher --extra all # All optional features\n```\n\n### From Source (Development)\n\n```bash\n# Clone and install\ngit clone https://github.com/contextualizer-ai/biosample-enricher.git\ncd biosample-enricher\nuv sync\n\n# With optional extras\nuv sync --extra mongodb # MongoDB support\nuv sync --extra metrics # Metrics and visualization\nuv sync --extra schema # Schema analysis tools\nuv sync --extra all # Everything\n```\n\n## Quick Start\n\n### Python API\n\nThe package exports 8 services from the top level:\n\n```python\nfrom biosample_enricher import (\n ElevationService,\n ElevationRequest,\n SoilService,\n WeatherService,\n MarineService,\n LandService,\n ReverseGeocodingService,\n ForwardGeocodingService,\n OSMFeaturesService,\n)\nfrom datetime import date\n\n# Get elevation for a location\nelevation_service = ElevationService()\nrequest = ElevationRequest(latitude=40.7128, longitude=-74.0060)\nobservations = elevation_service.get_elevation(request)\n\nfor obs in observations:\n if obs.value_numeric is not None:\n print(f\"{obs.provider.name}: {obs.value_numeric}m\")\n# Output:\n# usgs_3dep: 13.15m\n# google_elevation: 13.26m\n# open_topo_data: 25.0m\n# osm_elevation: 51.0m\n\n# Get weather data for a location and date\nweather_service = WeatherService()\nweather_result = weather_service.get_daily_weather(\n lat=37.7749,\n lon=-122.4194,\n target_date=date(2024, 1, 15)\n)\nprint(f\"Temperature: {weather_result.temperature.value}\u00b0C\")\nprint(f\"Precipitation: {weather_result.precipitation.value}mm\")\n\n# Get soil properties\nsoil_service = SoilService()\nsoil_result = soil_service.enrich_location(\n latitude=40.7128,\n longitude=-74.0060,\n depth_cm=\"0-5cm\"\n)\nprint(f\"Provider: {soil_result.provider}\")\nprint(f\"Quality score: {soil_result.quality_score}\")\n\n# Get marine data (SST, bathymetry, chlorophyll)\nmarine_service = MarineService()\nmarine_result = marine_service.get_comprehensive_marine_data(\n latitude=36.6,\n longitude=-121.9,\n target_date=date(2024, 1, 15)\n)\nif marine_result.sea_surface_temperature:\n print(f\"Sea surface temp: {marine_result.sea_surface_temperature.value}\u00b0C\")\nif marine_result.bathymetry:\n print(f\"Water depth: {marine_result.bathymetry.value}m\")\n\n# Reverse geocoding (coordinates -> place names)\ngeocoding_service = ReverseGeocodingService()\nresult = geocoding_service.reverse_geocode(lat=40.7128, lon=-74.0060)\nif result:\n print(f\"Location: {result.get_formatted_address()}\")\n\n# Get nearby geographic features\nosm_service = OSMFeaturesService()\nfeatures = osm_service.get_features_for_location(\n latitude=37.7749,\n longitude=-122.4194,\n radius_m=1000\n)\nif features and features.named_features:\n for feature in features.named_features[:5]:\n print(f\"{feature.name} ({feature.category}): {feature.distance_km:.2f}km\")\n```\n\n### CLI Usage\n\nEach service has its own CLI command:\n\n```bash\n# Elevation lookup\nuv run elevation-lookup lookup --lat 40.7128 --lon -74.0060\n\n# Soil data\nuv run soil-enricher lookup --lat 40.7128 --lon -74.0060 --depth 10\n\n# Weather data\nuv run weather-enricher lookup --lat 37.7749 --lon -122.4194 --date 2024-01-15\n\n# Marine data\nuv run marine-enricher lookup --lat 36.6 --lon -121.9 --date 2024-01-15\n\n# Land cover\nuv run land-enricher lookup --lat 40.7128 --lon -74.0060\n\n# Batch processing from CSV\nuv run elevation-lookup batch --input samples.csv --lat-col latitude --lon-col longitude\n\n# Version info\nuv run biosample-version\n```\n\n## Services\n\n### 1. Elevation Service\n\nGet elevation data from multiple providers (USGS, Google, Open Topo Data).\n\n**Providers**: USGS (US only, free), Google (global, requires API key), Open Topo Data (global, free)\n\n**Python**:\n```python\nfrom biosample_enricher import ElevationService, ElevationRequest\n\nservice = ElevationService()\nrequest = ElevationRequest(latitude=40.7128, longitude=-74.0060)\nobservations = service.get_elevation(request)\n```\n\n**CLI**:\n```bash\nuv run elevation-lookup lookup --lat 40.7128 --lon -74.0060\n```\n\n### 2. Soil Service\n\nGet soil properties (texture, pH, organic carbon, etc.).\n\n**Providers**: SoilGrids (global coverage), USDA NRCS (US only)\n\n**Python**:\n```python\nfrom biosample_enricher import SoilService\n\nservice = SoilService()\nsoil_result = service.enrich_location(\n latitude=40.7128,\n longitude=-74.0060,\n depth_cm=\"0-5cm\"\n)\n```\n\n**CLI**:\n```bash\nuv run soil-enricher lookup --lat 40.7128 --lon -74.0060 --depth 10\n```\n\n### 3. Weather Service\n\nGet historical weather data (temperature, precipitation, humidity, etc.).\n\n**Providers**: Open-Meteo (free, global), Meteostat (free, global)\n\n**Python**:\n```python\nfrom biosample_enricher import WeatherService\nfrom datetime import date\n\nservice = WeatherService()\nweather_result = service.get_daily_weather(\n lat=37.7749,\n lon=-122.4194,\n target_date=date(2024, 1, 15)\n)\n```\n\n**CLI**:\n```bash\nuv run weather-enricher lookup --lat 37.7749 --lon -122.4194 --date 2024-01-15\n```\n\n### 4. Marine Service\n\nGet marine data (sea surface temperature, bathymetry, chlorophyll).\n\n**Providers**: NOAA OISST (SST), GEBCO (bathymetry), ESA CCI (chlorophyll)\n\n**Python**:\n```python\nfrom biosample_enricher import MarineService\nfrom datetime import date\n\nservice = MarineService()\nmarine_result = service.get_comprehensive_marine_data(\n latitude=36.6,\n longitude=-121.9,\n target_date=date(2024, 1, 15)\n)\n```\n\n**CLI**:\n```bash\nuv run marine-enricher lookup --lat 36.6 --lon -121.9 --date 2024-01-15\n```\n\n### 5. Land Service\n\nGet land cover classification.\n\n**Providers**: ESA WorldCover, MODIS, NLCD (US only)\n\n**Python**:\n```python\nfrom biosample_enricher import LandService\n\nservice = LandService()\nland_result = service.enrich_location(\n latitude=40.7128,\n longitude=-74.0060\n)\n```\n\n**CLI**:\n```bash\nuv run land-enricher lookup --lat 40.7128 --lon -74.0060\n```\n\n### 6. Reverse Geocoding Service\n\nConvert coordinates to human-readable addresses.\n\n**Providers**: OSM Nominatim (free), Google Geocoding (requires API key)\n\n**Python**:\n```python\nfrom biosample_enricher import ReverseGeocodingService\n\nservice = ReverseGeocodingService()\nresult = service.reverse_geocode(lat=40.7128, lon=-74.0060)\nif result:\n print(result.get_formatted_address())\n```\n\n### 7. Forward Geocoding Service\n\nConvert addresses/place names to coordinates.\n\n**Providers**: OSM Nominatim (free), Google Geocoding (requires API key)\n\n**Python**:\n```python\nfrom biosample_enricher import ForwardGeocodingService\n\nservice = ForwardGeocodingService()\nresult = service.geocode(\"New York City\")\nif result and result.locations:\n for location in result.locations[:3]:\n print(f\"{location.formatted_address}: {location.latitude}, {location.longitude}\")\n```\n\n### 8. OSM Features Service\n\nGet nearby geographic features (parks, water bodies, landmarks).\n\n**Providers**: OpenStreetMap Overpass API (free), Google Places (requires API key)\n\n**Python**:\n```python\nfrom biosample_enricher import OSMFeaturesService\n\nservice = OSMFeaturesService()\nfeatures = service.get_features_for_location(\n latitude=37.7749,\n longitude=-122.4194,\n radius_m=1000\n)\nif features and features.named_features:\n for feature in features.named_features[:5]:\n print(f\"{feature.name} ({feature.category})\")\n```\n\n## API Keys\n\nOnly required for Google services (optional - OSM alternatives available for everything):\n\n```bash\n# Single API key for all Google services\nexport GOOGLE_MAIN_API_KEY=\"your-key-here\"\n```\n\nAll other services are free and require no authentication.\n\n## Development\n\n### Setup\n\n```bash\n# Clone repository\ngit clone https://github.com/contextualizer-ai/biosample-enricher.git\ncd biosample-enricher\n\n# Complete development setup\nmake dev-setup\n```\n\n### Testing\n\n```bash\n# Run fast tests (excludes network/slow tests)\nmake test-fast\n\n# Run all tests with coverage\nmake test-cov\n\n# Run specific test categories\nmake test-unit # Unit tests only\nmake test-integration # Integration tests\n```\n\n### Code Quality\n\n```bash\n# Format, lint, type-check, test\nmake dev-check\n\n# Full CI validation\nmake check-ci\n\n# Individual checks\nmake format # Format with ruff\nmake lint # Lint with ruff\nmake type-check # Type check with mypy\nmake dep-check # Check dependencies with deptry\n```\n\n## Project Structure\n\n```\nbiosample-enricher/\n\u251c\u2500\u2500 biosample_enricher/\n\u2502 \u251c\u2500\u2500 __init__.py # Public API exports\n\u2502 \u251c\u2500\u2500 elevation/ # Elevation service\n\u2502 \u251c\u2500\u2500 soil/ # Soil service\n\u2502 \u251c\u2500\u2500 weather/ # Weather service\n\u2502 \u251c\u2500\u2500 marine/ # Marine service\n\u2502 \u251c\u2500\u2500 land/ # Land cover service\n\u2502 \u251c\u2500\u2500 reverse_geocoding/ # Reverse geocoding\n\u2502 \u251c\u2500\u2500 forward_geocoding/ # Forward geocoding\n\u2502 \u251c\u2500\u2500 osm_features/ # Geographic features\n\u2502 \u251c\u2500\u2500 models.py # Core data models\n\u2502 \u251c\u2500\u2500 http_cache.py # HTTP caching\n\u2502 \u2514\u2500\u2500 cli*.py # CLI commands\n\u251c\u2500\u2500 tests/ # Test suite\n\u251c\u2500\u2500 pyproject.toml # Project configuration\n\u2514\u2500\u2500 Makefile # Development automation\n```\n\n## Dependencies\n\n### Core Dependencies\n- **Always installed**: pandas, rasterio, meteostat (required for weather aggregation and global soil coverage)\n- CLI and data validation: click, pydantic, requests, rich, pyyaml\n\n### Optional Dependencies\n- **mongodb**: `pymongo` for fetching from NMDC/GOLD databases (evaluation/demo only)\n- **metrics**: `matplotlib`, `seaborn` for visualization\n- **schema**: `genson` for schema analysis\n\nInstall with: `uv sync --extra mongodb` or `uv sync --extra all`\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Run checks (`make dev-check`)\n5. Commit (`git commit -m 'Add amazing feature'`)\n6. Push (`git push origin feature/amazing-feature`)\n7. Open a Pull Request\n\nSee [CLAUDE.md](CLAUDE.md) for detailed development guidelines.\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Built with [UV](https://github.com/astral-sh/uv) for fast package management\n- CLI powered by [Click](https://click.palletsprojects.com/)\n- Data validation with [Pydantic](https://pydantic.dev/)\n- Console output with [Rich](https://github.com/Textualize/rich)\n- Caching with [requests-cache](https://github.com/requests-cache/requests-cache)\n\n## Support\n\n- **Issues**: [GitHub Issues](https://github.com/contextualizer-ai/biosample-enricher/issues)\n- **Email**: info@contextualizer.ai\n",
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