ak-context-engineering

name	ak: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.
user-invocable	true
when_to_use	Invoke for context budget, memory, or agent architecture issues.
category	utilities
keywords	["context","tokens","limits","memory","optimization"]
argument-hint	[topic or question]
metadata	{"author":"agentkit","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

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