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.
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tessl review fix ./cli-tool/components/skills/ai-research/context-window-management/SKILL.mdContext 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
- Repository
davila7/claude-code-templates
- Revision
e7d17f9
- Last updated
- First committed
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