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name: context-engineering description: >- 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. version: 1.0.0

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

Quick Reference

Topic	When to Use	Reference
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
Pipelines	Project development, batch processing	project-development.md

Key Metrics

• Token utilization: Warning at 70%, trigger optimization at 80%
• Token variance: Explains 80% of agent performance variance
• Multi-agent cost: ~15x single agent baseline
• Compaction target: 50-70% reduction, <5% quality loss
• Cache hit target: 70%+ for stable workloads

Four-Bucket Strategy

1. Write: Save context externally (scratchpads, files)
2. Select: Pull only relevant context (retrieval, filtering)
3. Compress: Reduce tokens while preserving info (summarization)
4. Isolate: Split across sub-agents (partitioning)

Anti-Patterns

• Exhaustive context over curated context
• Critical info in middle positions
• No compaction triggers before limits
• Single agent for parallelizable tasks
• Tools without clear descriptions

Guidelines

1. Place critical info at beginning/end of context
2. Implement compaction at 70-80% utilization
3. Use sub-agents for context isolation, not role-play
4. Design tools with 4-question framework (what, when, inputs, returns)
5. Optimize for tokens-per-task, not tokens-per-request
6. Validate with probe-based evaluation
7. Monitor KV-cache hit rates in production
8. Start minimal, add complexity only when proven necessary

Scripts

• context_analyzer.py - Context health analysis, degradation detection
• compression_evaluator.py - Compression quality evaluation