Problem Statement
While Cursor AI IDE excels at generating documentation within chat and composer sessions, we currently lack a systematic way to capture and preserve this valuable context over time. The AI has deep understanding of code evolution within a session, including:
- Original requirements and intent -this changes over time within any chat or composer
- Step-by-step development decisions - llm memory loses historical accuracy
- Code modifications and their rationale - llm memory loses initial context
- Historical context of changes and reverts
- Complete implementation context
However, this rich context is often lost once the session ends or degrades over longer threads, leading to less comprehensive documentation, especially when generated in isolation.
Proposed Solution
Implement a context-aware documentation system that captures and preserves the full development journey from Cursor AI chat and composer sessions.
Core Functionality
1. Contextual Documentation Tracking
- Automatically track key development decisions and changes
- Preserve rationale behind code modifications
- Record implementation approaches considered
- Document any reverted changes and their reasons
- Maintain links between related code segments
2. GitHub-Compatible Output
- Generate documentation in GitHub-compatible markdown
- Support for GitHub Wiki formatting
- README.md generation with full context
- API documentation following GitHub conventions
- Integration with GitHub Pages
3. Development Flow Tracking
- Monitor code execution and terminal commands
- Track compilation and runtime outcomes
- Record test executions and results
- Log development patterns and approaches
- Identify redundant development paths
- Flag similar solutions attempted by team members
4. Documentation Management
- Local documentation storage with standardized structure
- Version control integration
- Documentation preview within IDE
- Export options for different documentation platforms
- Support for custom documentation templates
Documentation Standards
Base Standard: GitHub
- README.md
- Wiki documentation
- Code documentation
- API documentation
- Contributing guidelines
Extended Support
- Abstract documentation model that can be transformed for different platforms
- Template system for custom documentation formats
- Export adapters for popular documentation systems (e.g., Docusaurus)
Development Flow Tracking Features
1. Code Execution Monitoring
- Track terminal commands and their outcomes
- Record compilation results and error patterns
- Monitor test execution flows
- Log runtime behavior and performance metrics
- Capture debugging sessions and outcomes
2. Pattern Recognition
- Identify repeated development attempts
- Flag similar approaches across team members
- Detect common error patterns
- Track solution evolution over time
- Monitor resource usage patterns
3. Team Collaboration
- Share successful development paths
- Warn about previously attempted approaches
- Highlight efficient solutions found by team members
- Track knowledge sharing and reuse
- Identify opportunities for code reuse
Use Case Example
- Developer starts new feature implementation in Cursor AI
- AI assists with code generation and modifications
- System tracks:
- Initial requirements
- Implementation decisions
- Code changes and reverts
- Testing approaches
- Developer requests documentation generation
- System generates comprehensive docs with:
- Feature overview from initial context
- Implementation details with rationale
- Usage examples from development session
- API documentation with real usage context
- Links to related components discussed in session
Benefits
1. Documentation Quality
- More accurate and comprehensive documentation
- Preservation of development context
- Better explanation of implementation decisions
- Improved maintainability
2. Development Efficiency
- Automated documentation generation
- Reduced time spent on manual documentation
- Better knowledge transfer between team members
- Easier onboarding for new developers
3. Project Management
- Better tracking of feature development
- Clear documentation of technical decisions
- Improved project handover process
- Easier code review process
Implementation Example
A typical development tracking scenario:
-
Developer runs a terminal command:
npm install new-package- System logs command and outcome
- Records any dependency conflicts
- Tracks resolution steps
-
Developer executes test suite
- System captures test results
- Records performance metrics
- Logs any test failures and fixes
-
Code compilation attempt fails
- System logs error patterns
- Records successful resolution steps
- Flags similar past issues and solutions
-
Another team member attempts similar approach
- System identifies pattern match
- Suggests documented solution
- Shares previous learning path
This tracking ensures:
- No duplicate effort on solved problems
- Faster resolution of common issues
- Better knowledge sharing across team
- More efficient development patterns
Technical Considerations
- Efficient storage of session context
- Performance impact of continuous context tracking
- Integration with existing version control systems
- Support for documentation standards and templates
- Scalability for large projects
- Memory management for long development sessions
- Efficient storage of execution logs and patterns
- Performance impact of continuous execution tracking
- Security considerations for command and execution logging
- Integration with CI/CD pipelines
- Team permissions and access control for shared patterns
Priority Level
High - This feature would significantly improve documentation quality and development efficiency while leveraging Cursor AI’s unique context-aware capabilities.
Would appreciate the community’s thoughts on:
- Preferred documentation standards and formats
- Must-have features for documentation generation
- Integration preferences with existing documentation platforms
- Suggestions for managing documentation context in long-running sessions