
Context Window Management
This covers the practical stuff you need when your LLM app starts bumping into token limits or losing track of what matters in long conversations. It's about strategies like tiered context (different approaches based on size), smart summarization that prioritizes importance over recency, and avoiding the lost-in-the-middle problem where models miss info buried in long contexts. The anti-patterns call out naive truncation and one-size-fits-all approaches, which is helpful. If you're building anything with conversation memory or multi-turn dialogues, you'll hit these problems eventually. Pairs well with RAG and prompt caching skills.
- skills.sh installs
- 587
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill context-window-management --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
Sponsors
Details
- Repository
- github.com/davila7/claude-code-templates
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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