context-engineering

name	context-engineering
description	Check context usage limits, monitor time remaining, optimize token consumption, debug context failures. Use when asking about context percentage, rate limits, usage warnings, context optimization, agent architectures, memory systems.
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 Task agents
5. 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 Task agents.

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

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 Task 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 Task 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

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