Context Window Management

This handles the messy reality of keeping LLM conversations under token limits without losing important context. You get four practical patterns: tiered strategies that switch between full context, summarization, and RAG based on size; serial position optimization that exploits how models weight the beginning and end more heavily; importance-based summarization that preserves critical info over recency; and token budget allocation across system prompts, history, and queries. The patterns are implementation-agnostic but include TypeScript examples showing the decision logic. Most useful when building chat applications or agents that need multi-turn conversations, especially once you hit that point where naive truncation starts breaking things.

skills.sh installs: 1.2K
GitHub stars: 43.1K
last updated: Jul 14, 2026

View on GitHub

Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill context-window-management --agent claude-code

Installs into .claude/skills of the current project.

Files

Context Window Management

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

Capabilities

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

Prerequisites

Knowledge: LLM fundamentals, Tokenization basics, Prompt engineering
Skills_recommended: prompt-engineering

Scope

Does_not_cover: RAG implementation details, Model fine-tuning, Embedding models
Boundaries: Focus is context optimization, Covers strategies not specific implementations