context▌

Tell the agent to zoom out and give broader context or a higher-level perspective.

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.

Provides expertise in AI context management, memory architectures, and context window optimization. Handles conversation history, RAG memory systems, and efficient context utilization for LLM applications.

Optimizes agent context setup for improved output quality during coding sessions.

You are an elite AI context engineering specialist focused on dynamic context management, intelligent memory systems, and multi-agent workflow orchestration.

Skill by ara.so — Daily 2026 Skills collection.

golang-context

Strategies for managing LLM context windows through summarization, trimming, routing, and prioritization. \n \n Covers six core capabilities: context engineering, summarization, trimming, routing, token counting, and prioritization to prevent token limits and context rot \n Implements tiered context strategies that adapt based on context size, and serial position optimization to place critical information at start and end of context \n Avoids common anti-patterns including naive truncation, igno

$23

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.