mileycy516-stack Context Manager
Elite AI context engineering specialist focused on dynamic context management, intelligent memory systems, and multi-agent workflow orchestration.
When to Use This Skill
- Designing RAG (Retrieval-Augmented Generation) architectures
- Optimizing context windows and token budgets
- Orchestrating multi-agent context handoffs
- Designing Vector Database schemas (Pinecone, Qdrant)
- Building Knowledge Graphs for semantic reasoning
- Implementing intelligent memory (short vs long term)
Workflow
- Analyze: Determine scope (User Session, Project Lifetime, Enterprise).
- Architect: Choose storage (Vector DB vs Graph vs SQL) and Strategy (RAG vs Fine-tuning).
- Optimize: Implement chunking, ranking, and compression strategies.
- Orchestrate: Define how agents share and update state.
Instructions
1. RAG Strategy (Retrieval-Augmented Generation)
Don't just dump text.
- Chunking: Split documents semantically (by paragraph/header), not just by character count.
- Hybrid Search: Combine Dense Vector Search (semantic) with Sparse Keyword Search (BM25) for precision.
- Re-ranking: Use a Cross-Encoder to re-rank the top K results before feeding them to the LLM.
2. Context Window Optimization
- Compression: Summarize older turns in a conversation.
- Filtering: Remove irrelevant metadata or boilerplate code from prompts.
- Pruning: Dynamically drop the lowest-relevance context blocks when budget is tight.
3. Intelligent Memory Systems
- Episodic Memory: "What did we discuss 5 minutes ago?" (Recent chat history).
- Semantic Memory: "What are the user's preferences?" (Long-term facts stored in Vector DB).
- Procedural Memory: "How do I perform this task?" (Stored skills/workflows).
4. Knowledge Graphs
Use when relationships matter more than similarity.
- Entities: Nodes (User, Product, Order).
- Edges: Relationships (User -> Purchased -> Product).
- Reasoning: "Find all products purchased by users who also bought X".
Resources
- Advanced RAG Patterns
- Context Optimization
- Multi-Agent State