context-engineering

majiayu000

更新日 Today

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テストai

について

このスキルは、開発者がClaudeのコンテキストウィンドウ使用状況を監視・最適化し、障害を防止しコスト削減を支援します。使用制限の確認、問題のデバッグ、効率的なメモリまたはエージェントアーキテクチャの実装のためのツールを提供します。コンテキスト制約がパフォーマンスやレイテンシに影響するLLMパイプライン構築時にご利用ください。

クイックインストール

Claude Code

推奨

プラグインコマンド推奨

/plugin add https://github.com/majiayu000/claude-skill-registry

Git クローン代替

git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/context-engineering

このコマンドをClaude Codeにコピー＆ペーストしてスキルをインストールします

ドキュメント

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

IMPORTANT:

Sacrifice grammar for the sake of concision.
Ensure token efficiency while maintaining high quality.
Pass these rules to subagents.

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
Runtime Awareness	Usage limits, context window monitoring	runtime-awareness.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

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

Runtime Awareness

The system automatically injects usage awareness via PostToolUse hook:

<usage-awareness>
Claude Usage Limits: 5h=45%, 7d=32%
Context Window Usage: 67%
</usage-awareness>

Thresholds:

70%: WARNING - consider optimization/compaction
90%: CRITICAL - immediate action needed

Data Sources:

Usage limits: Anthropic OAuth API (https://api.anthropic.com/api/oauth/usage)
Context window: Statusline temp file (/tmp/ck-context-{session_id}.json)

Scripts

context_analyzer.py - Context health analysis, degradation detection
compression_evaluator.py - Compression quality evaluation

GitHub リポジトリ

majiayu000/claude-skill-registry

パス: skills/context-engineering