todo.md

Code Repository Content Marketing System - Implementation Checklist

Phase 1: Project Setup and Core Infrastructure

Step 1: Basic Project Structure

• Create project directory structure
• Set up Python virtual environment (Python 3.12)
• Create requirements.txt with appropriate dependencies:
  • LiteLLM
  • MCP client
  • Loguru
  • Pytest
  • Asyncio-related packages
• Create setup.py for packaging
• Write initial README.md with project description
• Create main entry point script (main.py)
• Set up version control (git)
• Set up initial gitignore file

Step 2: Logging and Configuration System

• Implement logging system using Loguru:
  • Set up appropriate log levels
  • Configure formatted output
  • Implement file rotation
  • Add error capturing
• Create configuration system:
  • Add YAML/JSON config file loading
  • Implement sensible defaults
  • Add configuration validation
  • Support environment variable overrides
  • Create user preference options:
    • Platform selection settings
    • Content tone preferences
    • Approval workflow settings
    • Publication frequency options

Step 3: Basic CLI Interface

• Set up CLI framework (Click or Typer)
• Create main entry point with subcommands
• Implement commands:
  • Repository analysis command
  • Content generation command
  • Content publishing command
  • Settings configuration command
  • Log viewing command
• Add help text and usage examples
• Implement input validation and error handling
• Integrate CLI with logging system
• Integrate CLI with configuration system

Phase 2: Repository Analysis

Step 4: Repository Parser Foundation

• Create repository analyzer module
• Implement local repository path handling
• Add repository type detection (git, etc.)
• Implement metadata extraction:
  • Repository name
  • Description
  • Branch information
• Create directory scanner and file tree builder
• Implement file type categorization
• Add error handling for invalid repositories
• Integrate with logging system
• Connect to CLI commands

Step 5: Code File Parsing

• Implement language detection
• Create base language parser interface
• Implement language-specific parsers:
  • Python parser
  • JavaScript parser
  • One additional language parser
• Add extraction functionality:
  • Function and class definitions
  • Comments and docstrings
  • Import statements and dependencies
  • Basic code metrics
• Create structured data format for parsed code
• Add error handling for parsing issues
• Integrate with repository parser foundation

Step 6: Documentation and README Parsing

• Implement Markdown parser
• Implement reStructuredText parser
• Create README extraction:
  • Project descriptions
  • Feature lists
  • Installation instructions
  • Usage examples
  • API documentation
• Add general documentation parsing:
  • Architecture descriptions
  • Technical decisions
  • API references
  • Tutorial content
• Create structured data format for documentation
• Add error handling for different formats
• Integrate with repository parser foundation

Step 7: Technical Feature Identification

• Create analysis module for technical features
• Implement detection for:
  • Architectural patterns
  • Technology stack components
  • Unique implementation approaches
  • Performance optimizations
  • Testing strategies
  • Developer experience features
• Add feature scoring system:
  • Uniqueness scoring
  • Complexity assessment
  • Audience interest potential
• Implement confidence level system
• Create structured output format for content generation
• Integrate with repository parser components

Phase 3: Content Generation

Step 8: Content Generation Foundation

• Create base content generator
• Implement LiteLLM integration
• Add content template support
• Create prompt engineering system
• Implement context management
• Design modular platform system
• Design content type system
• Add user customization options
• Create initial prompt templates
• Add error handling and logging
• Test with repository analysis output

Step 9: Platform-Specific Content Adaptation

• Create platform adapter interface
• Implement Dev.to adapter:
  • Technical article formatting
  • Code sample handling
  • Header and image support
• Implement Twitter/X adapter:
  • Character limit handling
  • Hashtag support
  • Thread creation
• Implement LinkedIn adapter:
  • Professional formatting
  • Business value focus
• Add platform-specific constraints:
  • Size/character limitations
  • Formatting requirements
  • Platform features (hashtags, mentions)
  • Tone adjustments
• Create platform-specific prompt templates
• Integrate with content generation foundation

Step 10: Content Type Templates

• Create template interface
• Implement templates:
  • Project introduction template
  • Technical deep-dive template
  • Architecture breakdown template
  • Feature highlight template
  • Release notes template
• Add template-specific:
  • Prompt engineering strategies
  • Structure guidelines
  • Tone and focus instructions
  • Content selection logic
• Create template selection system
• Integrate with platform-specific adapters

Step 11: AI Enhancement Capabilities

• Implement specialized modules:
  • Technical explanation generator
  • Code sample extractor and explainer
  • Architecture diagram describer
  • Performance benefit quantifier
  • Business value articulator
• Create dynamic prompt adjustment:
  • Repository analysis integration
  • User preference handling
  • Platform targeting
  • Adaptation based on previous results
• Add quality checking:
  • Technical accuracy validation
  • Readability scoring
  • Engagement potential assessment
• Integrate with content generation system

Phase 4: User Interaction

Step 12: Interview Mode Implementation

• Create interview system
• Implement missing information detection
• Add question generation
• Create user response processor
• Add adaptive questioning based on previous answers
• Implement question types:
  • Open-ended context questions
  • Technical decision questions
  • Clarification questions
  • Preference questions
• Create CLI interface for interview process
• Integrate with repository analysis
• Integrate with content generation

Step 13: Content Review Interface

• Create content preview system:
  • Platform-specific preview rendering
  • Format visualization
  • Key point highlighting
• Implement content editing:
  • Text editing capabilities
  • Format validation
  • Improvement suggestions
• Add version comparison
• Create user-friendly interface
• Implement error handling and feedback
• Integrate with content generation system

Step 14: Approval Workflow

• Create workflow manager
• Implement workflow options:
  • Automatic publishing
  • Manual approval
  • Approval routing
  • Notification system
• Add approval states:
  • Draft state
  • Pending approval state
  • Approved state
  • Rejected state with feedback
  • Published state
  • Failed state
• Implement approval persistence
• Create approval history tracking
• Integrate with configuration system
• Integrate with CLI
• Connect with content review system

Phase 5: Integration and Publication

Step 15: MCP Integration

• Set up MCP client:
  • Client configuration
  • Initialization process
  • Authentication handling
  • Request/response management
  • Error handling and retries
• Implement Context7 tool integration
• Add any other relevant MCP tools
• Create abstraction layer
• Test MCP connectivity
• Add comprehensive error handling
• Integrate with existing code

Step 16: Publication Functionality

• Create publisher interface
• Implement platform publishers:
  • Dev.to publisher
  • Twitter/X publisher
  • LinkedIn publisher
• Add authentication for each platform
• Implement content formatting for APIs
• Add metadata handling:
  • Tags
  • Categories
  • Other platform-specific metadata
• Create publication status tracking
• Implement error handling and retries
• Add publication verification
• Add publication confirmation
• Integrate with MCP
• Connect with approval workflow

Step 17: Scheduling System

• Create scheduler component
• Implement scheduling options:
  • One-time scheduling
  • Recurring publication patterns
  • Platform-specific timing optimization
  • Queue management
• Add scheduling persistence
• Implement background worker
• Create CLI commands for schedule management
• Add reliability features
• Implement recovery from interruptions
• Integrate with publication system
• Connect with approval workflow

Step 18: Error Handling and Retry Logic

• Create centralized error system:
  • Error categorization
  • Recovery strategy assignment
  • User notification for critical errors
• Implement retry mechanisms:
  • Exponential backoff
  • Maximum retry limits
  • Circuit breakers
• Add queue systems:
  • Content generation retry queue
  • Publication retry queue
  • API rate limit handling queue
• Apply consistent error handling
• Focus on external integration robustness
• Test failure scenarios
• Document error handling strategies

Phase 6: Testing and Refinement

Step 19: Unit and Integration Testing

• Create unit tests:
  • Repository parser tests
  • Content generator tests
  • Publication integration tests
  • User interaction tests
• Implement integration tests:
  • End-to-end workflow tests
  • API interaction tests with mocks
  • Configuration validation tests
• Add test fixtures:
  • Sample repository fixtures
  • AI response mocks
  • Platform API mocks
• Set up pytest configuration
• Ensure good test coverage
• Prepare for CI/CD integration

Step 20: End-to-End Testing

• Create end-to-end test scenarios:
  • Complete workflow tests
  • Error recovery tests
  • Configuration change tests
  • User interaction flow tests
• Add tests with real repositories
• Implement sandbox tests for platforms
• Create automated test pipeline
• Document test scenarios
• Address edge cases
• Prepare for CI/CD integration

Step 21: Performance Optimization

• Profile system for bottlenecks:
  • Repository parsing performance
  • AI generation latency
  • Identify parallelization opportunities
• Implement caching:
  • Repository analysis cache
  • AI query cache
  • API response cache
• Add performance monitoring
• Focus on real-world improvements
• Maintain reliability during optimization
• Document optimization strategies
• Benchmark before and after

Step 22: Documentation and Final Polish

• Create user documentation:
  • Installation guide
  • Setup instructions
  • Usage documentation
  • Configuration options
  • Troubleshooting guide
• Write developer documentation:
  • Architecture overview
  • Component descriptions
  • Extension points
  • Contributing guidelines
• Add final polish:
  • Code cleanup
  • Consistency improvements
  • User experience enhancements
  • Error message refinement
  • Log output optimization
• Conduct final review
• Address any remaining issues
• Prepare for release

Final Review

• Verify all components work together
• Confirm all requirements from spec are met
• Test on multiple repositories of varying complexity
• Gather user feedback
• Make final adjustments
• Prepare for initial release