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context-engineering

@thangchung/agent-engineering-experiment
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SKILL.md

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