vccontext-engineering

# Context Engineering Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage. ## When to Activate - Designing/debugging agent systems - Context limits constrain performance - Optimizing cost/latency - Building multi-agent coordination - Implementing memory systems - Evaluating agent performance - Developing LLM-powered pipelines ## Core Principles 1. **Context quality > quantity** - High-signal tokens beat exhaustive content 2. **Attention is finite** - U-shaped curve favors beginning/end positions 3. **Progressive disclosure** - Load information just-in-time 4. **Isolation prevents degradation** - Partition work across sub-agents 5. **Measure before optimizing** - Know your baseline **IMPORTANT:** - Sacrifice grammar for the sake of concision. - Ensure token efficiency while maintaining high quality. - Pass these rules to subagents. ## Quick Reference | Topic | When to Use | Reference | |-------|-------------|-----------| | **Fundamentals** | Understanding context anatomy, attention mechanics | [context-fundamentals.md](./references/context-fundamentals.md) | | **Degradation** | Debugging failures, lost-in-middle, poisoning | [context-degradation.md](./references/context-degradation.md) | | **Optimization** | Compaction, masking, caching, partitioning | [context-optimization.md](./references/context-optimization.md) | | **Compression** | Long sessions, summarization strategies | ...