Agent skill · research science · davila7
context-window-management
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.
Why this skill is useful
Provides domain-specific strategies for managing LLM context windows that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/context-window-management