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