Individual skill
context-engineering
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
skills/engineering/context-engineering/SKILL.md
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SKILL.mdcore · 4,035 bytesreferences/context-compression.mddoc · 2,335 bytesreferences/context-degradation.mddoc · 3,094 bytesreferences/context-fundamentals.mddoc · 2,732 bytesreferences/context-optimization.mddoc · 2,333 bytesreferences/evaluation.mddoc · 2,253 bytesreferences/memory-systems.mddoc · 2,570 bytesreferences/multi-agent-patterns.mddoc · 2,319 bytesreferences/project-development.mddoc · 2,142 bytesreferences/tool-design.mddoc · 2,057 bytesscripts/compression_evaluator.pyruns · 10,940 bytesscripts/context_analyzer.pyruns · 10,334 bytes
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# 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
