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example-tasks.json
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{
"available_tasks": [
{
"id": "python-web-1",
"title": "Implement async database operations with SQLAlchemy",
"description": "Convert synchronous database operations to async using SQLAlchemy 2.0 async features",
"priority": "high",
"estimated_effort": "large",
"required_skills": ["devops"],
"files_to_modify": ["models/", "database.py", "api/"],
"success_criteria": [
"Async session factory configured",
"All database queries converted to async",
"Connection pooling optimized",
"Performance benchmarks show improvement"
]
},
{
"id": "python-web-2",
"title": "Add Pydantic models for API validation",
"description": "Implement Pydantic models for request/response validation in FastAPI endpoints",
"priority": "medium",
"estimated_effort": "medium",
"required_skills": [],
"files_to_modify": ["schemas/", "api/endpoints/"],
"success_criteria": [
"Pydantic models for all endpoints",
"Automatic validation enabled",
"Clear error messages",
"OpenAPI schema auto-generated"
]
},
{
"id": "python-web-3",
"title": "Implement Redis caching layer",
"description": "Add Redis caching for frequently accessed data to improve API performance",
"priority": "medium",
"estimated_effort": "medium",
"required_skills": ["devops"],
"files_to_modify": ["cache.py", "api/", "docker-compose.yml"],
"success_criteria": [
"Redis connection configured",
"Cache decorator implemented",
"TTL strategy defined",
"Cache invalidation logic added"
]
},
{
"id": "python-data-1",
"title": "Create data pipeline with Apache Airflow",
"description": "Build ETL pipeline for processing daily data imports using Airflow DAGs",
"priority": "high",
"estimated_effort": "large",
"required_skills": ["data", "devops"],
"files_to_modify": ["dags/", "plugins/", "config/airflow.cfg"],
"success_criteria": [
"DAG created with proper dependencies",
"Error handling and retries configured",
"Monitoring and alerting set up",
"Data quality checks implemented"
]
},
{
"id": "python-ml-1",
"title": "Deploy ML model with FastAPI and Docker",
"description": "Create production-ready API endpoint for machine learning model inference",
"priority": "high",
"estimated_effort": "medium",
"required_skills": ["ml-engineer", "devops"],
"files_to_modify": ["api/predict.py", "models/", "Dockerfile"],
"success_criteria": [
"Model loaded efficiently",
"Batch prediction support",
"Input validation and preprocessing",
"Response time < 100ms"
]
}
]
}