AgentSkillsCN

context-engineering

掌握 AI 智能体系统的上下文工程设计方法。适用于设计智能体架构、调试上下文异常、优化 Token 使用、部署记忆系统、构建多智能体协作机制、评估智能体性能，或开发基于大语言模型的全流程工作流时使用。内容涵盖上下文基础理论、退化模式、优化技巧、压缩策略、记忆架构、多智能体协作模式、性能评估、工具设计，以及项目开发全流程。

SKILL.md
--- frontmatter
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, compression strategies, memory architectures, multi-agent patterns, evaluation, tool design, and project development.
license: MIT

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

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 clear descriptions (what, when, inputs, returns)
  5. •Optimize for tokens-per-task, not tokens-per-request
  6. •Validate with probe-based evaluation
  7. •Monitor token usage in production
  8. •Start minimal, add complexity only when proven necessary

Skill Coordination

When multiple skills are active:

  • •Load only relevant skill content
  • •Use skill metadata for discovery
  • •Avoid loading full skill definitions unless needed
  • •Reference skills by pattern detection, not direct names

References

For detailed guidance, see:

  • •references/fundamentals.md - Context anatomy, attention mechanics
  • •references/degradation.md - Debugging failures, lost-in-middle, poisoning
  • •references/optimization.md - Compaction, masking, caching, partitioning
  • •references/compression.md - Long sessions, summarization strategies
  • •references/memory.md - Cross-session persistence, knowledge graphs
  • •references/multi-agent.md - Coordination patterns, context isolation
  • •references/evaluation.md - Testing agents, LLM-as-Judge, metrics
  • •references/tool-design.md - Tool consolidation, description engineering