context-engineering

SKILL.md

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.

| Fundamentals | Understanding context anatomy, attention mechanics | context-fundamentals.md | | Degradation | Debugging failures, lost-in-middle, poisoning | context-degradation.md | | Optimization | Compaction, masking, caching, partitioning | context-optimization.md | | Compression | Long sessions, summarization strategies | context-compression.md |

| Memory | Cross-session persistence, knowledge graphs | memory-systems.md | | Multi-Agent | Coordination patterns, context isolation | multi-agent-patterns.md | | Evaluation | Testing agents, LLM-as-Judge, metrics | evaluation.md | | Tool Design | Tool consolidation, description engineering | tool-design.md |

Master context engineering for AI agents - token optimization, degradation patterns, compression, memory systems, multi-agent coordination, evaluation. Use when designing agents, debugging context failures, or building LLM pipelines. Source: vibery-studio/templates.

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