Auto-generates paper_config.yaml by intelligently detecting modules, analyzing their purpose, and creating a comprehensive configuration for research paper generation.
"Dakini" or "dakini""Generate paper config""Create paper configuration""Setup paper generation"Dakini automatically scans your codebase to detect modules, analyzes their purpose and importance, and generates a comprehensive paper_config.yaml file. This eliminates manual configuration and ensures all relevant modules are included in your research paper generation workflow.
Scan Source Directory:
# Example: Scan src/nikhil/amsha/ or src/{package}/
base_path = "src/nikhil/amsha"
modules = [d for d in listdir(base_path) if is_directory(d)]
Filter Criteria:
.py files (not just __init__.py)__pycache__, .pytest_cache, testsFor each discovered module, analyze:
Priority Detection:
# Critical: Contains domain models, core algorithms, or "main" logic
# High: Contains business logic, orchestration, or processing
# Medium: Contains utilities, factories, or helpers
# Low: Contains purely auxiliary functions
Indicators:
core.py, engine.py, repository.py, or contains Pydantic modelsmanager.py, processor.py, service.pyfactory.py, builder.py, adapter.pyutils.py, helpers.py, common.pyDescription Generation:
Read the module's __init__.py docstring or first .py file's module-level docstring to extract purpose.
Focus Areas Identification:
# Scan for common patterns:
- "repository" → repository_pattern
- "Protocol" class → clean_architecture
- "Builder" suffix → builder_pattern
- "Factory" suffix → factory_pattern
- "manager" → orchestration
- Statistical functions → algorithms
Template:
# Paper Generation Configuration
# Auto-generated by Dakini on {timestamp}