---
name: Context Window Management
slug: context-window-management
category: AI Engineering
description: Context Window Management provides strategies for managing LLM context windows, including summarization, trimming, routing, and avoiding context rot. Use it when hitting token limits, losing critical information mid-dialogue, or tuning long-context applications.
github: "https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/context-window-management"
language: Python
stars: 30236
forks: 3396
install: "npx degit https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/context-window-management ~/.claude/skills/context-window-management"
installs_to: ~/.claude/skills/context-window-management
source_path: cli-tool/components/skills/ai-research/context-window-management/SKILL.md
collection_size: 25
category_size: 2451
collection_url: "https://dirskills.com/collections/davila7/claude-code-templates"
added: 2026-08-14T07:11:47.256Z
last_synced: 2026-08-14T07:11:47.256Z
canonical_url: "https://dirskills.com/skills/context-window-management"
---

# Context Window Management

Context Window Management provides strategies for managing LLM context windows, including summarization, trimming, routing, and avoiding context rot. Use it when hitting token limits, losing critical information mid-dialogue, or tuning long-context applications.

**Install:**

```bash
npx degit https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/context-window-management ~/.claude/skills/context-window-management
```

## README

# 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`