Cline rules

1# Documentation Requirements
2 
3## Documentation Standards
4Maintain comprehensive documentation that supports both developers and AI assistants working on this project.
5 
6### Update Requirements
7- **README.md**: Update when adding significant functionality or changing project scope
8- **AI Configuration Files**: Keep all AI assistant configuration files current as project evolves
9- **Environment Documentation**: Maintain `.env.example` with all required variables documented
10- **API Documentation**: Document all AI service integrations and expected responses
11 
12### AI Service Documentation
13When integrating AI services, document:
14 
15#### Service Configuration
16```markdown
17## OpenAI Integration
18- **Model**: GPT-4 (configurable via environment)
19- **Rate Limits**: 500 requests/hour per API key
20- **Required Environment Variables**:
21  - `OPENAI_API_KEY`: Your OpenAI API key
22  - `OPENAI_MODEL`: Model to use (default: gpt-4)
23  - `OPENAI_MAX_TOKENS`: Maximum tokens per request (default: 2048)
24```
25 
26#### Usage Examples
27Always include practical examples:
28```javascript
29// Example: Basic AI chat completion
30const completion = await openai.chat.completions.create({
31  model: process.env.OPENAI_MODEL || "gpt-4",
32  messages: [
33    { role: "system", content: "You are a helpful AI assistant." },
34    { role: "user", content: userPrompt }
35  ],
36  max_tokens: parseInt(process.env.OPENAI_MAX_TOKENS) || 2048
37});
38```
39 
40#### Error Scenarios
41Document common error patterns and handling:
42```markdown
43### Common Errors
44- **401 Unauthorized**: Invalid API key or expired credentials
45- **429 Rate Limited**: Too many requests, implement exponential backoff
46- **500 Server Error**: AI service temporarily unavailable
47- **Context Length Exceeded**: Input too long for model context window
48```
49 
50## Code Documentation
51- Use JSDoc comments for complex AI algorithms and integrations
52- Document function parameters and return types for AI service calls
53- Include examples in code comments for non-obvious AI implementations
54- Explain AI model choice rationale and configuration decisions
55 
56## Changelog Maintenance
57When adding AI functionality, update documentation with:
58- New AI service integrations
59- Model version changes or updates
60- API endpoint modifications
61- Breaking changes to AI interfaces
62- Performance improvements or optimizations
63 
64## Architecture Decision Records (ADRs)
65For major AI-related decisions, create ADRs documenting:
66- Why specific AI models or services were chosen
67- Trade-offs between different AI providers
68- Performance and cost considerations
69- Security and privacy implications
70 
71## Team Knowledge Sharing
72- Document AI service setup steps for new team members
73- Include troubleshooting guides for common AI service issues
74- Maintain examples of successful AI integration patterns
75- Share lessons learned from AI service limitations and workarounds