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context-engineering

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.

mrgoonie/claudekit-skills
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含まれるファイル（17）

  • SKILL.md3.9 KB
  • references/context-compression.md2.3 KB
  • references/context-degradation.md3.0 KB
  • references/context-fundamentals.md2.7 KB
  • references/context-optimization.md2.3 KB
  • references/evaluation.md2.2 KB
  • references/memory-systems.md2.5 KB
  • references/multi-agent-patterns.md2.3 KB
  • references/project-development.md2.1 KB
  • references/tool-design.md2.0 KB
  • scripts/compression_evaluator.py10.7 KB
  • scripts/context_analyzer.py10.1 KB
  • tests/01-basic-context-optimization.md632 B
  • tests/02-debug-lost-in-middle.md640 B
  • tests/03-multi-agent-coordination.md748 B
  • tests/04-edge-case-context-poisoning.md724 B
  • tests/05-advanced-memory-architecture.md816 B

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

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