SKILL.md
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
- Context quality > quantity - High-signal tokens beat exhaustive content
- Attention is finite - U-shaped curve favors beginning/end positions
- Progressive disclosure - Load information just-in-time
- Isolation prevents degradation - Partition work across sub-agents
- Measure before optimizing - Know your baseline
Quick Reference
Topic
When to Use
Reference
Fundamentals
Understanding context anatomy, attention mechanics
Degradation
Debugging failures, lost-in-middle, poisoning
Optimization
Compaction, masking, caching, partitioning
Compression
Long sessions, summarization strategies
Memory
Cross-session persistence, knowledge graphs
Multi-Agent
Coordination patterns, context isolation
Evaluation
Testing agents, LLM-as-Judge, metrics
Tool Design
Tool consolidation, description engineering
Pipelines
Project development, batch processing
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
- Write: Save context externally (scratchpads, files)
- Select: Pull only relevant context (retrieval, filtering)
- Compress: Reduce tokens while preserving info (summarization)
- 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
- Place critical info at beginning/end of context
- Implement compaction at 70-80% utilization
- Use sub-agents for context isolation, not role-play
- Design tools with 4-question framework (what, when, inputs, returns)
- Optimize for tokens-per-task, not tokens-per-request
- Validate with probe-based evaluation
- Monitor KV-cache hit rates in production
- Start minimal, add complexity only when proven necessary
Scripts
- context_analyzer.py - Context health analysis, degradation detection
- compression_evaluator.py - Compression quality evaluation