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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

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

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

Weekly Installs

252

Repository

mrgoonie/claude…t-skills

GitHub Stars

1.9K

First Seen

Jan 22, 2026

Security Audits

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Installed on

opencode200

claude-code199

gemini-cli195

codex181

cursor167

github-copilot159