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.

Stars

27,681

Source

davila7/claude-code-templates

Updated

2026-05-31

Slug

davila7--claude-code-templates--context-window-management

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/davila7/claude-code-templates/HEAD/cli-tool/components/skills/ai-research/context-window-management/SKILL.md -o .claude/skills/context-window-management.md

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