# Context Window Management

> Strategies for managing LLM context windows including summarization, trimming, routing, and avoid…

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.

- Page: https://ss33.ai/skills/davila7/context-window-management-63512857633
- Publisher: davila7
- Category: [AI & Agents](https://ss33.ai/skills/category/ai-and-agents-y6jz7i) › [Memory & context](https://ss33.ai/skills/category/ai-and-agents-y6jz7i/memory-and-context-5m28a5)
- License: MIT
- Source: https://github.com/davila7/claude-code-templates/blob/da2b74e9059820e70ad07ff2d2b5321371ab1709/cli-tool/components/skills/ai-research/context-window-management/SKILL.md

## Skill

SKILL.md: https://ss33.ai/api/v1/ss33/download/davila7/context-window-management-63512857633

# 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`