Files
wild-cloud/.ai/docs/ai-context.md

389 lines
8.4 KiB
Markdown

# AI Context Management Guide
Master the art of feeding AI the right information at the right time for optimal results.
## 📚 Overview
The AI context system helps you:
- Provide consistent reference materials to your AI assistant
- Generate comprehensive project documentation
- Manage external library documentation
- Organize project-specific context
- Maintain philosophy alignment
## 🗂️ Directory Structure
```
ai_context/ # Persistent reference materials
├── README.md # Directory documentation
├── IMPLEMENTATION_PHILOSOPHY.md # Core development philosophy
├── MODULAR_DESIGN_PHILOSOPHY.md # Architecture principles
├── generated/ # Auto-generated project docs
│ └── [project-rollups] # Created by build_ai_context_files.py
└── git_collector/ # External library docs
└── [fetched-docs] # Created by build_git_collector_files.py
ai_working/ # Active AI workspace
├── README.md # Usage instructions
├── [feature-folders]/ # Feature-specific context
└── tmp/ # Temporary files (git-ignored)
└── [scratch-files] # Experiments, debug logs, etc.
```
## 🎯 Quick Start
### 1. Generate Project Context
```bash
# Generate comprehensive project documentation
make ai-context-files
# Or run directly
python tools/build_ai_context_files.py
```
This creates rollup files in `ai_context/generated/` containing:
- All source code organized by type
- Configuration files
- Documentation
- Test files
### 2. Add External Documentation
```bash
# Fetch library documentation
python tools/build_git_collector_files.py
# Configure libraries in git_collector_config.json
{
"libraries": [
{
"name": "react",
"repo": "facebook/react",
"docs_path": "docs/"
}
]
}
```
### 3. Load Philosophy
```
# In your AI assistant
/prime
# Or manually reference
Please read @ai_context/IMPLEMENTATION_PHILOSOPHY.md and follow these principles
```
## 🧠 Philosophy Documents
### IMPLEMENTATION_PHILOSOPHY.md
Core principles that guide all development:
- **Simplicity First**: Clean, maintainable code
- **Human-Centric**: AI amplifies, doesn't replace
- **Pragmatic Choices**: Real-world solutions
- **Trust in Emergence**: Let good architecture emerge
### MODULAR_DESIGN_PHILOSOPHY.md
Architecture principles for scalable systems:
- **Bricks & Studs**: Self-contained modules with clear interfaces
- **Contract-First**: Define interfaces before implementation
- **Regenerate, Don't Patch**: Rewrite modules when needed
- **AI-Ready**: Design for future automation
### Using Philosophy in Prompts
```
/ultrathink-task Build a user authentication system following our philosophy:
@ai_context/IMPLEMENTATION_PHILOSOPHY.md
@ai_context/MODULAR_DESIGN_PHILOSOPHY.md
Focus especially on simplicity and contract-first design.
```
## 📋 Context Generation Tools
### build_ai_context_files.py
Generates comprehensive project documentation:
```python
# Default configuration
FILE_GROUPS = {
"Source Code": {
"patterns": ["**/*.py", "**/*.js", "**/*.ts"],
"exclude": ["**/test_*", "**/*.test.*"]
},
"Configuration": {
"patterns": ["**/*.json", "**/*.yaml", "**/*.toml"],
"exclude": ["**/node_modules/**"]
}
}
```
**Features**:
- Groups files by type
- Respects .gitignore
- Adds helpful headers
- Creates single-file rollups
**Customization**:
```python
# In build_ai_context_files.py
FILE_GROUPS["My Custom Group"] = {
"patterns": ["**/*.custom"],
"exclude": ["**/temp/**"]
}
```
### collect_files.py
Core utility for pattern-based file collection:
```bash
# Collect all Python files
python tools/collect_files.py "**/*.py" > python_files.md
# Collect with exclusions
python tools/collect_files.py "**/*.ts" --exclude "**/node_modules/**" > typescript.md
```
### build_git_collector_files.py
Fetches external documentation:
```bash
# Configure libraries
cat > git_collector_config.json << EOF
{
"libraries": [
{
"name": "fastapi",
"repo": "tiangolo/fastapi",
"docs_path": "docs/",
"include": ["tutorial/", "advanced/"]
}
]
}
EOF
# Fetch documentation
python tools/build_git_collector_files.py
```
## 🎨 Best Practices
### 1. Layer Your Context
```
Base Layer (Philosophy)
Project Layer (Generated docs)
Feature Layer (Specific requirements)
Task Layer (Current focus)
```
### 2. Reference Strategically
```
# Good: Specific, relevant context
@ai_context/generated/api_endpoints.md
@ai_working/auth-feature/requirements.md
# Avoid: Everything at once
@ai_context/**/*
```
### 3. Keep Context Fresh
```bash
# Update before major work
make ai-context-files
# Add to git hooks
echo "make ai-context-files" >> .git/hooks/pre-commit
```
### 4. Use Working Spaces
```
ai_working/
├── feature-x/
│ ├── requirements.md # What to build
│ ├── decisions.md # Architecture choices
│ ├── progress.md # Current status
│ └── blockers.md # Issues to resolve
└── tmp/
└── debug-session-1/ # Temporary investigation
```
## 🔧 Advanced Techniques
### Dynamic Context Loading
```
# Load context based on current task
/ultrathink-task I need to work on the API layer.
Load relevant context:
@ai_context/generated/api_*.md
@ai_context/api-guidelines.md
```
### Context Templates
Create reusable context sets:
```bash
# .ai/contexts/api-work.md
# API Development Context
## Load these files:
- @ai_context/generated/api_routes.md
- @ai_context/generated/models.md
- @ai_context/api-standards.md
- @docs/api/README.md
## Key principles:
- RESTful design
- Comprehensive error handling
- OpenAPI documentation
```
### Incremental Context
Build context progressively:
```
# Start broad
Read @ai_context/IMPLEMENTATION_PHILOSOPHY.md
# Get specific
Now read @ai_context/generated/auth_module.md
# Add requirements
Also consider @ai_working/auth-v2/requirements.md
```
### Context Versioning
Track context evolution:
```bash
# Version generated docs
cd ai_context/generated
git add .
git commit -m "Context snapshot: pre-refactor"
```
## 📊 Context Optimization
### Size Management
```python
# In build_ai_context_files.py
MAX_FILE_SIZE = 100_000 # Skip large files
MAX_ROLLUP_SIZE = 500_000 # Split large rollups
```
### Relevance Filtering
```python
# Custom relevance scoring
def is_relevant(file_path: Path) -> bool:
# Skip generated files
if 'generated' in file_path.parts:
return False
# Skip vendor code
if 'vendor' in file_path.parts:
return False
# Include based on importance
important_dirs = ['src', 'api', 'core']
return any(d in file_path.parts for d in important_dirs)
```
### Context Caching
```bash
# Cache expensive context generation
CONTEXT_CACHE=".ai/context-cache"
CACHE_AGE=$(($(date +%s) - $(stat -f %m "$CONTEXT_CACHE" 2>/dev/null || echo 0)))
if [ $CACHE_AGE -gt 3600 ]; then # 1 hour
make ai-context-files
touch "$CONTEXT_CACHE"
fi
```
## 🎯 Common Patterns
### Feature Development
```
1. Create feature workspace:
mkdir -p ai_working/new-feature
2. Add requirements:
echo "..." > ai_working/new-feature/requirements.md
3. Generate fresh context:
make ai-context-files
4. Start development:
/ultrathink-task Implement @ai_working/new-feature/requirements.md
```
### Debugging Sessions
```
1. Capture context:
echo "Error details..." > ai_working/tmp/debug-notes.md
2. Add relevant code:
python tools/collect_files.py "**/auth*.py" > ai_working/tmp/auth-code.md
3. Analyze:
Help me debug using:
@ai_working/tmp/debug-notes.md
@ai_working/tmp/auth-code.md
```
### Documentation Updates
```
1. Generate current state:
make ai-context-files
2. Update docs:
Update the API documentation based on:
@ai_context/generated/api_routes.md
3. Verify consistency:
/review-code-at-path docs/
```
## 🚀 Pro Tips
1. **Front-Load Philosophy**: Always start with philosophy docs
2. **Layer Gradually**: Add context as needed, not all at once
3. **Clean Regularly**: Remove outdated context from ai_working
4. **Version Important Context**: Git commit key snapshots
5. **Automate Generation**: Add to build pipelines
## 🔗 Related Documentation
- [Command Reference](commands.md) - Commands that use context
- [Philosophy Guide](philosophy.md) - Core principles