Enhanced Just-In-Time document loading system that intelligently discovers, loads, and caches relevant documentation based on user intent and project context. Use when users need specific documentation, when working with new technologies, when answering domain-specific questions, or when context indicates documentation gaps.
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Enhanced Just-In-Time document loading system that intelligently discovers, loads, and caches relevant documentation based on user intent and project context. Use when users need specific documentation, when working with new technologies, when answering domain-specific questions, or when context indicates documentation gaps.
Advanced Just-In-Time documentation loading system that intelligently discovers, retrieves, and caches relevant documentation based on user intent, project context, and knowledge gaps. Enhances Alfred's capabilities by bringing in the right documentation at the right time.
Core capabilities:
✅ Intent-based document discovery
✅ Context-aware loading strategies
✅ Intelligent caching and retrieval
✅ Multi-source documentation aggregation
✅ Domain-specific knowledge integration
✅ Real-time web research for latest information
✅ Documentation quality assessment
✅ Automatic relevance ranking
When to Use
✅ When user questions indicate knowledge gaps
✅ When working with unfamiliar technologies or frameworks
✅ When domain-specific expertise is needed
✅ When answering technical questions with precision
✅ When implementing new features or patterns
✅ When troubleshooting complex issues
✅ When best practices guidance is required
Intent Detection Patterns
1. Question-Based Triggers
# User asks specific questions"how do I implement JWT in FastAPI?""What's the best way to handle database migrations?""How does React useEffect work exactly?""What are the security implications of JWT?"# Trigger: Load relevant documentation
Skill("moai-jit-docs-enhanced")
# → Loads FastAPI JWT authentication docs# → Loads database migration best practices# → Loads React hooks documentation# → Loads JWT security analysis
defload_documentation_on_demand():
"""Load documentation only when needed"""# Don't preload everything# Load based on user interaction# Cache for future usepass
3. Batch Processing
defbatch_web_searches(queries):
"""Combine multiple searches for efficiency"""# Group similar queries# Use WebSearch for multiple terms# Process results in parallelpass
Error Handling
1. Network Failures
if web_search_fails():
# Fall back to cached content# Use local documentation# Provide partial results
2. Content Quality Issues
if content_quality_low():
# Try alternative sources# Indicate uncertainty# Request user clarification
3. Relevance Mismatches
if relevance_score < threshold:
# Refine search query# Ask user for clarification# Broaden search scope
Usage Statistics and Learning
1. Track Effectiveness
deftrack_documentation_usage(doc_id, user_feedback):
"""Learn which documentation is most helpful"""
stats[doc_id] = {
"usage_count": stats[doc_id]["usage_count"] + 1,
"helpful_votes": stats[doc_id]["helpful_votes"] + user_feedback,
"contexts": stats[doc_id]["contexts"].append(current_context)
}
2. Improve Source Selection
defimprove_source_ranking():
"""Learn which sources work best for different contexts"""# Analyze usage patterns# Adjust source priorities# Personalize recommendations
End of Skill | Intelligent documentation loading for enhanced context and accuracy
