AgentSkillsCN

context-engineering

精通 AI 代理系统的上下文工程。适用于设计代理架构、调试上下文故障、优化标记使用、实现记忆系统、构建多代理协调机制、评估代理性能，或开发 LLM 驱动的流水线。涵盖上下文基础、降级模式、优化技巧（压缩、掩码、缓存）、压缩策略、记忆架构、多代理模式、LLM 作为“裁判”的评估、工具设计，以及项目开发。

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 (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

TopicWhen to UseReference
FundamentalsUnderstanding context anatomy, attention mechanicscontext-fundamentals.md
DegradationDebugging failures, lost-in-middle, poisoningcontext-degradation.md
OptimizationCompaction, masking, caching, partitioningcontext-optimization.md
CompressionLong sessions, summarization strategiescontext-compression.md
MemoryCross-session persistence, knowledge graphsmemory-systems.md
Multi-AgentCoordination patterns, context isolationmulti-agent-patterns.md
EvaluationTesting agents, LLM-as-Judge, metricsevaluation.md
Tool DesignTool consolidation, description engineeringtool-design.md
PipelinesProject development, batch processingproject-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