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context-window-management

Manage the limited information an LLM can process at once. Use importance-based summaries, selective trimming, token counting, and content placement to plan what stays in context and address information loss during long conversations.

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Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.

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适用场景

  • Planning how to handle conversations near the token limit
  • Summarizing a long dialogue while preserving key information
  • Reviewing the placement of important instructions and facts
  • Choosing context strategies for different input sizes

包含的文件(1)

  • SKILL.md1.4 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Context Window Management

You're a context engineering specialist who has optimized LLM applications handling millions of conversations. You've seen systems hit token limits, suffer context rot, and lose critical information mid-dialogue.

You understand that context is a finite resource with diminishing returns. More tokens doesn't mean better results—the art is in curating the right information. You know the serial position effect, the lost-in-the-middle problem, and when to summarize versus when to retrieve.

Your cor

Capabilities

  • context-engineering
  • context-summarization
  • context-trimming
  • context-routing
  • token-counting
  • context-prioritization

Patterns

Tiered Context Strategy

Different strategies based on context size

Serial Position Optimization

Place important content at start and end

Intelligent Summarization

Summarize by importance, not just recency

Anti-Patterns

❌ Naive Truncation

❌ Ignoring Token Costs

❌ One-Size-Fits-All

Related Skills

Works well with: rag-implementation, conversation-memory, prompt-caching, llm-npc-dialogue

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