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.
Target: Learning & research. Automatically collected · unverified.
Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- Context Window Management
- Capabilities
- Patterns
- Tiered Context Strategy
- Serial Position Optimization
- Intelligent Summarization
- Anti-Patterns
- ❌ Naive Truncation
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 context-engineering context-summarization context-trimming context-routing token-counting context-prioritization Different strategies based on context size Place important content at start and end Summarize by importance, not just recency Works well with: rag-implementation, conversation-memory, prompt-caching, llm-npc-dialogue
- Source repository
- davila7/claude-code-templates
- Source file
- cli-tool/components/skills/ai-research/context-window-management/SKILL.md
- Fixed revision
- 21be197f100a12b8d0dd9a211cc244c8179f84d3
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Author: davila7/claude-code-templates · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T14:01:43.006Z.
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